ALBERT

Description: Time–frequency packing (TFP) transmission provides the highest achievable spectral efficiency with a constrained symbol alphabet and detector complexity. In this paper, the application of the TFP technique to fiber-optic systems is investigated and experimentally demonstrated. The main theoretical aspects, design guidelines, and implementation issues are discussed, focusing on those aspects which are peculiar to TFP systems. In particular, adaptive compensation of propagation impairments, matched filtering, and maximum a posteriori probability detection are obtained by a combination of a two-dimensional equalizer and four eight-state parallel Bahl–Cocke–Jelinek–Raviv (BCJR) detectors. A novel algorithm that ensures adaptive equalization, channel estimation, and a proper distribution of tasks between the equalizer and BCJR detectors is proposed. A set of irregular low-density parity-check codes with different rates is designed to operate at low error rates and approach the spectral efficiency limit achievable by TFP at different signal-to-noise ratios. An experimental demonstration of the designed system is finally provided with five dual-polarization QPSK-modulated optical carriers, densely packed in a 100-GHz bandwidth, employing a recirculating loop to test the performance of the system at different transmission distances.

Description: We investigated nonlinear optical characteristics of Tungsten disulfide (WS 2 ) films and experimentally demonstrated their high potential for application as nonlinear saturable absorbers in passively mode-locked fiber lasers. Side polished fiber (SPF) was fabricated and WS 2 film was overlaid to provide an efficient evanescent field interaction. The WS 2 film was prepared using two methods: liquid phase exfoliation to form few-layer nano-sheets, and chemical vapor deposition (CVD) to grow uniform multilayer WS 2 on a SiO 2 substrate. Two SPF saturable absorbers were prepared by either spin coating WS 2 solution or lifting off the multilayer CVD WS 2 on SPF. An all-fiber ring cavity was built and the WS 2 film overlaid on SPF was employed as a mode locker along with Er-doped fiber as a gain medium. Using the spin-coated WS 2 SPF, stable soliton-like pulses were generated with a spectral width of 5.6 nm and 467 fs pulse duration. The fiber laser cavity containing CVD WS2 SPF generated a transform-limited soliton pulse train with a spectral width of 8.23 nm and a pulse duration of 332 fs. Our study confirmed a high potential of WS 2 film as a novel 2-D nonlinear optical material for laser applications.

Description: We propose and experimentally demonstrate an on-chip all-optical differential-equation solver capable of solving second-order ordinary differential equations (ODEs) characterizing continuous-time linear time-invariant systems. The photonic device is implemented by a self-coupled microresonator on a silicon-on-insulator platform with mutual coupling between the cavity modes. Owing to the mutual mode coupling within the same resonant cavity, the resonance wavelengths induced by different cavity modes are self-aligned, thus avoiding precise wavelength alignment and unequal thermal wavelength drifts as in the case of cascaded resonators. By changing the mutual mode coupling strength, the proposed device can be used to solve second-order ODEs with tunable coefficients. System demonstration using the fabricated device is carried out for 10-Gb/s optical Gaussian and super-Gaussian input pulses. The experimental results are in good agreement with theoretical predictions of the solutions, which verify the feasibility of the fabricated device as a tunable second-order photonic ODE solver.

Description: We demonstrate a 100 Gb/s short reach system using a multicarrier transmitter based on a gain switched monolithically integrated laser. An optical comb source with 12.5-GHz free spectral range is achieved by gain-switching an integrated passive feedback laser. The 100 Gb/s wavelength division multiplexed, single sideband, direct detection, orthogonal frequency division multiplexed (WDM-SSB-DD-OFDM) system operates over 25 km standard single mode fiber exhibiting a spectral efficiency of 1.8 b/s/Hz. Receiver sensitivity of –14.2 dBm is achieved after 25 km transmission. Performance optimization with phase and amplitude precompensation is employed to improve the SSB OFDM modulation thereby reducing the interchannel interference and overcoming the power fading induced by the optical filter. We also present a theoretical analysis of the SSB-OFDM modulation.

Description: In this paper, narrow-band emission lines are generated by means of two random distributed feedback fiber laser schemes. Spectral line-widths as narrow as 3.2 pm have been measured, which significantly improves previous reported results. The laser is analyzed with the aim of obtaining a spectral line-width as narrow as possible. Additionally a variation of this setup for multi-wavelength operation is also validated. Both schemes present a simple topology that use a combination of phase-shifted fiber Bragg gratings and regular fiber Bragg gratings as filtering elements.

Description: We report on the fabrication and characterization of 19-cell hypocycloid-shape Kagome fibers with core size larger than 100 μm. These inhibited coupling fibers present low propagation loss (100 dB/km) over broad transmission range with low chromatic dispersion combined with ultra-low power overlap with silica surround, making them an efficient solution for ultra-high power laser handling, ultra-fast laser delivery, and plasma photonics applications.

Description: The iterative demodulation and decoding algorithm introduced in 2005 by Colavolpe, Barbieri, and Caire to cope with channels affected by phase noise needs pilot symbols to bootstrap. However, pilot symbols reduce the spectral efficiency of the system and, consequently, system's throughput. The aim of this paper is to show that trellis-based demodulation can be used to bootstrap the iterative process without the need of pilot symbols. Also, the complexity issue of trellis-based demodulation is addressed in this paper. The result is that the performance of iterative demodulation and decoding after the iterations is virtually unaffected by complexity reduction, provided that the reduced-complexity demodulator guarantees cycle-slip-free operation. From the numerical results presented in this paper, we show that cycle-slip-free operation can be achieved with substantial complexity reduction also for phase noise associated with linewidths of practical interest.

Description: The optical pulse evolution in a highly nonlinear normal dispersion-increasing fiber has been considered, both experimentally and theoretically. It was found that large spectral broadening in tapered waveguides could occur without temporal instabilities and impose the linear frequency modulation, i.e., chirp, required for high-quality pulse compression. The pedestal-free pulses have been demonstrated after dechirping in a standard single-mode fiber.

Description: This work proposes a novel localized surface plasmon resonance (LSPR) biochemical sensor featuring high sensitivity and a high resolution. The sensor was divided into two subcomponents according to their distinct functions; namely, single-mode fiber and metal array. Single-mode fibers located on the left and right sides of the sensors function as the input and output for optical fiber signals. A metal array comprising an arrangement of cylindrical nanometal particles served as the detection area of the sensor. To effectively reduce the memory capacity and calculation time, two innovative techniques (i.e., object meshing and boundary meshing) were integrated with the finite element method. With the area of the triangular elements used as a basis, the object boundary, small object, medium object, and large objects were meshed at a ratio of 1:8:160:1600. The improved numerical simulation methods and six design procedures were adopted to develop and analyze the proposed LSPR biochemical sensor. The results show that the novel LSPR biochemical sensor outperformed two current high-performance biochemical sensors and provided additional advantages such as short length (approximately 430 μm), high resolution (approximately –120 dB), and high sensitivity (approximately 127 604 nm/RIU).

Description: We propose an ultra-broadband super light absorber by integrating different-sized tapered hyperbolic metamaterial (HMM) waveguides, each of which has a different and wide absorption band due to broadband slow-light response, into a unit cell. We numerically demonstrate that such an absorber is superior to a single-sized HMM absorber in terms of absorption bandwidth, while maintaining a comparable absorption efficiency. A three different-sized HMM absorber presents the capability of working with an ultra-wide frequency band ranging from 1 to 30 THz, which is much larger than previously proposed absorbers working in the same spectral region. Such a design shows great promise for a broad range of applications such as thermal emitters, photovoltaics, optical-chemical energy harvesting, and stealth technology, where ultra-wideband absorption is in very high demand.

Description: This paper proposes a ring-based integrated wireless optical network architecture and an associated protocol that involves Ethernet passive optical network (EPON) and long term evolution (LTE) wireless network. The architecture along with the proposed protocol is instrumental toward the reduction of handover delay. The proposed ring-based EPON architecture facilitates the implementation of the X2 interface for LTE network by enabling the optical network units of the EPON backhaul to directly communicate with each other. The work further discusses about an open access network architecture where a single EPON can be used by multiple mobile service providers without compromising information security. Extensive simulations have been carried out to evaluate the performance of the proposed network. An analytical model has been introduced to calculate the queuing delay experienced by the X2 interface. The model has been validated with the simulations results.

Description: High spectrum efficiency and fast restoration speed are highly desired for survivable elastic optical networks (EONs). In this paper, we take the advantages of failure-independent path-protecting preconfigured cycles (FIPP p -cycles) and investigate how to realize spectrum efficient resilience design with them. We first study the problem of offline service provisioning with FIPP p -cycles. We formulate an integer linear programming model and prove that the problem is $mathcal{NP}$ hard. Then, several time-efficient heuristics are designed for FIPP p -cycle formulation and related routing, modulation format, and spectrum assignment. Extensive simulations on offline provisioning verify that the heuristics can obtain near-optimal solutions. Next, we consider online service provisioning with FIPP p -cycles in dynamic EONs. In order to overcome the decrease of protection efficiency during dynamic network operation, we propose a p -cycle reconfiguration scheme to reoptimize protection structures on-the-fly. Simulation results demonstrate that the proposed algorithms can improve spectrum efficiency and reduce bandwidth blocking probability simultaneously.

Description: Photo-thermo-refractive (PTR) glasses are the class of polyfunctional materials that combine the properties of several monofunctional materials such as photo-refractive, laser, luminescent, and plasmonic ones. Based on PTR glasses, various diffractive holographic elements and photonic devices were developed in both the volume and fiber versions. In this paper, the fabrication of optical planar waveguides on PTR glass by low-temperature ion exchange is reported for the first time. Planar waveguides were fabricated through substituting the sodium ions in glass by silver, potassium, rubidium, and cesium ones from the nitrate melts. The silver waveguides were shown to have the largest depth (27 μm) and reveal no birefringence. For the silver waveguides, an increase in the refractive index is caused by differences in the polarizabilities of cations exchanged. The maximum increment of the refractive index was observed in the cesium waveguides (0.0512). An increase in the refractive index and also appearing the birefringence in potassium, rubidium, and cesium waveguides are found to be due to the compressive mechanical stresses and their relaxation. The potentialities of the ion exchange technology for fabricating, in PTR glasses, planar gradient waveguides with low losses (0.5 dB/cm) are demonstrated, the potentialities extending the application field of PTR glasses in photonics.

Description: A multi-sensor addressing method for fiber Bragg grating aided fiber loop ringdown (FBG-FLRD) sensor array is proposed. It is capable of simultaneous measurement of temperature and force. Light from a wavelength-swept fiber laser (WSFL) is modulated into pulsed light to illuminate FBG-FLRD sensor array. Based on the time sequence of pulsed lights reflected by fiber Bragg grating array, each FBG-FLRD sensor can be distinguished. The time interval between the trigger signal of the WSFL and the pulsed light reflected by FBG offers temperature variation information. By measuring the ringdown time of each FBG-FLRD, the loss of fiber ring induced by temperature and force can be obtained. And then simultaneous measurement can be implemented in real time. To prove the validity of the proposed system, a six elements array is experimentally validated with interrogation frequency of 27.5 Hz.

Description: Acoustic localization is an essential technique in speech capturing, speech enhancement, video conferencing, and human–robot interaction. However, in practical situations, localization has to be performed in abominable environments, where the presence of reverberation and noise degrades the performance of available position estimates. Besides, the designed systems should be adaptive to locomotion of targets with low computational complexity. In the context, this paper introduces a robust hierarchical acoustic localization method via time-delay compensation (TDC) and interaural matching filter (IMF). Firstly, interaural time-delay (ITD) and interaural level difference (ILD), which are cues involved in first two layers, respectively, are yielded by TDC all at once. Then, a novel feature named IMF, which can eliminate the difference between binaural signals, is proposed in the third layer. The final decision making is based on a Bayesian rule. The relationships among the three layers are that the former layer provides candidate directions for later ones such that the searching space becomes gradually smaller to reduce matching time. Experiments using both a public database and a real scenario verify that TDC and IMF are robust for acoustic localization, and hierarchical system has less consumption time.

Description: Canonical correlation analysis (CCA) is a widely used data analysis tool that allows to assess the correlation between two distinct sets of signals. It computes optimal linear combinations of the signals in both sets such that the resulting signals are maximally correlated. The weight vectors defining these optimal linear combinations are referred to as “principal CCA directions”. In addition to this particular type of data analysis, CCA is also often used as a blind source separation (BSS) technique, i.e., under certain assumptions, the principal CCA directions have certain demixing properties. In this paper, we propose a distributed CCA (DCCA) algorithm that can operate in wireless sensor networks (WSNs) with a fully connected or a tree topology. The algorithm estimates the $Q$ principal CCA directions from the sensor signal observations collected by the different nodes in the WSN and extracts the corresponding sources. These network-wide principal CCA directions are estimated in a time-recursive fashion without explicitly constructing the corresponding network-wide correlation matrices, i.e., without the need for data centralization. Instead, each node locally computes smaller CCA problems and only transmits compressed sensor signal observations (of dimension $Q$ ), which significantly reduces the bit rate over the wireless links of the WSN. We prove convergence and optimality of the DCCA algorithm, and we demonstrate its performance by means of numerical simulations in a blind source separation scenario.

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Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: There has been much research on shrinkage methods for real-valued covariance matrices and their inverses (precision matrices). In spectral analysis of $p$ -vector-valued time series, complex-valued spectral matrices and precision matrices arise, and good shrinkage methods are often required, most notably when the estimated complex-valued spectral matrix is singular. As an improvement on the Ledoit-Wolf (LW) type of spectral matrix estimator we use random matrix theory to derive a Rao-Blackwell estimator for a spectral matrix, its inverse being a Rao–Blackwellized estimator for the spectral precision matrix. A random matrix method has previously been proposed for complex-valued precision matrices. It was implemented by very costly simulations. We formulate a fast, completely analytic approach. Moreover, we derive a way of selecting an important parameter using predictive risk methodology. We show that both the Rao–Blackwell estimator and the random matrix estimator of the precision matrix can substantially outperform the inverse of the LW estimator in a time series setting. Our new methodology is applied to EEG-derived time series data where it is seen to work well and deliver substantial improvements for precision matrix estimation.

Description: In this paper, the state estimation problem for discrete-time linear systems influenced by multiplicative and time-correlated additive measurement noises is considered where the multiplicative noises are zero-mean white noise sequences, and the time-correlated additive noise is described by a linear system model with white noise. An optimal linear estimator for the system under consideration is proposed, which does not require computing the inverse of state transition matrix. The proposed estimator has a recursive structure, and has time-independent computation and storage load. Computer simulations are carried out to demonstrate the performance of the proposed estimator. The simulation results show the superiority of the proposed estimator.

Description: In this paper, we start with the standard support vector machine (SVM) formulation and extend it by considering a general SVM formulation with normalized margin. This results in a unified convex framework that allows many different variations in the formulation with very diverse numerical performance. The proposed unified framework can capture the existing methods, i.e., standard soft-margin SVM, $ell_{1}$ -SVM, and SVMs with standardization, feature selection, scaling, and many more SVMs, as special cases. Furthermore, our proposed framework can not only provide us with more insights on different SVMs from the “energy” and “penalty” point of views, which help us understand the connections and differences between them in a unified way, but also enable us to propose more SVMs that outperform the existing ones under some scenarios.

Description: We consider multiple-antenna signal detection of primary user transmission signals by a secondary user receiver in cognitive radio networks. The optimal detector is analyzed for the scenario where the number of primary user signals is no less than the number of receive antennas at the secondary user. We first derive exact expressions for the moments of the generalized likelihood ratio test (GLRT) statistic, yielding approximations for the false alarm and detection probabilities. We then show that the normalized GLRT statistic converges in distribution to a Gaussian random variable when the number of antennas and observations grow large at the same rate. Further, using results from large random matrix theory, we derive expressions to compute the detection probability without explicit knowledge of the channel, and then particularize these expressions for two scenarios of practical interest: 1) a single primary user sending spatially multiplexed signals, and 2) multiple spatially distributed primary users. Our analytical results are finally used to obtain simple design rules for the signal detection threshold.

Description: This paper presents an optimized low-complexity and high-throughput MIMO signal detector core for detecting spatially multiplexed data streams. The core architecture supports various layer configurations up to 4, while achieving near-optimal performance, and configurable modulation constellations up to 256-QAM on each layer. The core is capable of operating as a soft-input soft-output log-likelihood ratio (LLR) MIMO detector which can be used in the context of iterative detection and decoding. High area-efficiency is achieved via algorithmic and architectural optimizations performed at two levels. First, distance computations and slicing operations for an optimal 2-layer maximum a posteriori MIMO detector are optimized to eliminate use of multipliers and reduce the overhead of slicing in the presence of soft-input LLRs. We show that distances can be easily computed using elementary addition operations, while optimal slicing is done via efficient comparisons with soft decision boundaries, resulting in a simple feed-forward pipelined architecture. Second, to support more layers, an efficient channel decomposition scheme is presented that reduces the detection of multiple layers into multiple 2-layer detection subproblems, which map onto the 2-layer core with a slight modification using a distance accumulation stage and a post-LLR processing stage. Various architectures are accordingly developed to achieve a desired detection throughput and run-time reconfigurability by time-multiplexing of one or more component cores. The proposed core is applied also to design an optimal multiuser MIMO detector for LTE. The core occupies an area of 1.58 MGE and achieves a throughput of 733 Mbps for 256-QAM when synthesized in 90-nm CMOS.

Description: We study a tandem of agents who make decisions about an underlying binary hypothesis, where the distribution of the agent observations under each hypothesis comes from an uncertainty class defined by a 2-alternating capacity. We investigate both decentralized detection rules, where agents collaborate to minimize the error probability of the final agent, and social learning rules, where each agent minimizes its own local minimax error probability. We then extend our results to the infinite tandem network, and derive necessary and sufficient conditions on the uncertainty classes for the minimax error probability to converge to zero when agents know their positions in the tandem. On the other hand, when agents do not know their positions in the network, we study the cases where agents collaborate to minimize the asymptotic minimax error probability, and where agents seek to minimize their worst-case minimax error probability (over all possible positions in the tandem). We show that asymptotic learning of the true hypothesis is no longer possible in these cases, and derive characterizations for the minimax error performance.

Description: Various blind synchronization methods built on the maximum likelihood (ML) principle have been proposed, where the addressed scenarios include additive white Gaussian noise (AWGN), single-path fading, and multipath fading channels. We consider ML blind synchronization over wide-sense stationary uncorrelated scattering (WSSUS) channels. Different from existing studies, we exploit a more complete signal correlation function and find the carrier frequency offset estimate to be the solution of a quartic equation, rather than the phase angle of a complex number. As the truly ML synchronizer (dubbed MLE) is very complicated, we also derive a reduced-complexity alternative (dubbed RCE). It is found that the RCE yields indistinguishable performance from the MLE, at a somewhat lower complexity than an existing rival. We also present an in-depth theoretical analysis and comparison of the performance of various methods. Simulations show that the proposed methods yield rather robust performance in modeling errors of the fading rate and the channel power-delay profile (PDP).

Description: We report the results of a study of an optical sensor based on a channel-drop technique with two cascaded cavities in photonic-crystal slabs. Quality factors and intensities of the resonant modes of the sensor were analyzed with three-dimensional simulations. With the introduction of a reflector in the bus-channel and by control of the coupling between the two cavities and the drop-channel, the drop efficiency can be remarkably increased. In addition to the simulation, the two cavity sensor is fabricated and tested for optical response to water and oil infiltration. Both direct visual imaging and quantitative analysis were applied in experiment. A difference of refractive index $Delta n = 0.12$ between water and oil samples results in a wavelength shift of 18.3 nm, which greatly matches the simulation result of 20 nm and indicates a sensitivity of 153 nm RIU −1 . Both resonant peaks for water and oil infiltration have good selectivity in their transmission spectrum. The contrast between the broadband output of the bus-channel and the highly wavelength-selective outputs of the drop-channel opens opportunities for the two cascaded-cavity system as a fundamental building block for a multiplex drop-channel array for all-optical sensing, which can be widely used for bio/chemical detection and environmental monitoring.

Description: Nonlinearity-induced phase noise has become a major obstacle in long-haul coherent fiber-optic communication systems. Such phase noise has been shown to be signal dependent, and correlated over time. We propose a code-aided expectation-maximization algorithm to mitigate such nonlinear phase noise, iteratively utilizing both the time correlation of the nonlinearity-induced impairments and a soft-decision error-control code. Simulation and experimental results show that on a dual-polarization wavelength-division-multiplexed 16 QAM system, launch-power tolerance can be increased by ${1.5}$ dB, and the optical signal-to-noise ratio requirement can be relaxed by ${0.3}$ dB to achieve the same ${Q^2}$ -factor.

Description: Microring resonators have been the fundamental building blocks of integrated photonic networks. They play key roles in a number of on-chip photonic elements, such as filters, modulators, switches, and sensors. However, the applications of microring resonators in these components are hindered by the evanescent coupling, which requires very precise position control and is hard to be realized in standard photolithography. Here, we demonstrate a novel, general, and robust mechanism to achieve microdisk-based end-fire injection and collection devices. Instead of coupling light via evanescent waves, here the light is injected into the cavity by connecting a waveguide and a deformed microdisk directly. The light on resonance can be reflected back and detected at the same waveguide, making the devices to be “easy come easy go.” The reflectance is as high as 95% and can be periodically obtained from the long-lived resonances with free spectral range around 18 nm. The proposed mechanism is found to be robust to cavity shape, refractive index, and waveguide width and all the parameters can be larger than 500 nm, making the designed devices to be easier realized by standard photolithography. We believe this research will boost the development of low-cost silicon photonic devices.

Description: We proposed theoretically analyzed and experimentally demonstrated novel 1 × 4 channel flatband optical multiplexer/demultiplexer (MUX/DeMUX) that is based on Si-nanowire microring-assisted multiple delaylines. The proposed optical MUX/DeMUX scheme, which includes all-pass microring-type phase controllers and a Banyan-type 4 × 4 coupler, exhibits superior spectral flatness to any Si-nanowire optical DeMUXs that have previously been experimentally demonstrated.

Description: Analysis of the experimental data on microscopic, absorptive, fluorescent, and Raman-scattering properties of Bismuth (Bi)-doped yttria-alumino-silicate glass (Y-Al-SiO 2 :Bi)-based nanoengineered optical fibers, exhibiting broadband near-infrared fluorescence, is presented. Among the other well-established characteristics, inherent to Bi-doped silica fibers codoped with Aluminum (Al), a trend of spatial distributions of Bi atoms and Bi-related fluorescence-active centers is determined, being their concentrating in ring-like areas around the core's center, at approximately a half-distance to core/cladding interface. At the same time, the formation in this region of nanosized Bi clusters, supposedly weakly or nonfluorescing, is revealed for the fibers. These phenomena are argued to underlie worsening of the pump-to-signal overlap factor, which deteriorates efficiency of lasers and amplifiers based on such or similar Bi-doped alumino-silicate fibers.

Description: The paper develops a statistical model for the signals received in phase-sensitive optical time domain reflectometry (OTDR) probed by highly coherent sources. The backscattering process is modelled by a set of discrete scatterers with properly chosen parameters. Explicit equations for calculating the amplitude and the phase of the backscattered signal are obtained. The developed model predicts spectral and autocorrelation characteristics of the amplitude signals that are validated by experimental results. Characteristics of the phase signals, practicable for studying the sensing applications of the OTDR system, are presented and studied as well, demonstrating good correspondence with experiment. A more detailed modelling of distributed vibration sensing systems and their response to disturbances along an optical fiber will be possible as an extension of the developed formalism.

Description: A five channel step index plastic optical fiber proposal for a multiplexer/demultiplexer having insertion losses ( IL ) of 2.9–4 dB, pass bandwidths at −3 dB > 30 nm, crosstalk attenuation >30 dB and size of ∼65 mm × 55 mm, is demonstrated. It is based on a reflective diffraction grating with blazed profile and an aspheric lens. The theoretical analysis presented is used to further reduce the system size to ∼37 mm × 30 mm and to increase the number of channels to 8 keeping ILs 〈 4.5 dB. Experimental results have good agreement with theoretical expectations.

Description: In this paper, the performance of cloud radio access networks (CRANs) where spatially distributed remote radio heads (RRHs) aid the macro base station (MBS) in transmission is analysed. In order to reflect a realistic scenario, the MBS and the RRHs are assumed to be equipped with multiple antennas and distributed according to a Poisson point process. Both, the MBS and the RRHs, are assumed to employ maximal ratio transmission (MRT) or transmit antenna selection (TAS). Considering downlink transmission, the outage performance of three schemes is studied; first is the selection transmission (ST) scheme, in which the MBS or the RRH with the best channel is selected for transmission. In the second scheme, all the RRHs participate (ARP) and transmit the signal to the user, whereas in the third scheme, a minimal number of RRHs, to attain a desired data-rate, participate in transmission (MRP). Exact closed-form expression for the outage probability is derived for the ST scheme. For the ARP and MRP schemes, analytical approximations of the outage probability are derived which are tight at high signal-to-noise ratios. In addition, for the MRP scheme, the minimal number of RRHs required to meet a target data rate is also calculated which can be useful in characterizing the system complexity. Furthermore, the derived expressions are validated through numerical simulation. It is shown that the average diversity gains of these schemes are independent of the intensity/number of RRHs and only depend on the number of antennas on the MBS. Furthermore, the ARP scheme outperforms the ST scheme when the MBS/RRHs transmit with maximum power. However, in case of a sum power constraint and equal power allocation, the ST scheme outperforms the ARP scheme.

Description: This paper proposes a dynamic resource allocation scheme to exploit the mixed timescale channel state information (CSI) knowledge structure in a multi-antenna base station-assisted device-to-device (D2D) network. The short-term multi-antenna beamforming control at each transmit device is adaptive to the local real-time CSI. The long-term routing and flow control is adaptive to the global topology and the long-term global CSI statistics of the D2D network. The design objective is to maximize a network utility function subject to the average transmit power constraint, the flow balance constraints and the instantaneous physical layer capacity constraints. The mixed timescale problem can be decomposed into a short-term beamforming control problem and a long-term flow and routing control problem. Using the stochastic cutting plane, we propose a low complexity, self-learning algorithm, which converges to the global optimal solution without explicit knowledge of the channel statistics. Simulation illustrates performance gains with several baselines.

Description: This paper considers the estimation of multi-scale multi-lag (MSML) channels. The MSML channel model is a good representation for wideband communication channels, such as underwater acoustic communication and radar. This model is characterized by a limited number of paths, each parameterized by a delay, Doppler scale, and attenuation factor. Herein, it is shown that an OFDM signal after passing through the MSML channel exhibits a low rank representation. This feature can be exploited to improve the channel estimation. By characterizing the received signal, it is shown that the MSML channel estimation problem can be adapted to a structured spectral estimation problem. The challenge is that the unknown frequencies are very close to each other due to the small values of Doppler scales. This feature can be employed to show that the data matrix is approximately low-rank. By exploiting structural features of the received signal, the Prony algorithm is modified to estimate the Doppler scales (close frequencies), delays and channel gains. Two strategies using convex and no-convex regularizers to remove noise from the corrupted signal are proposed. These algorithms are iterative based on the alternating direction method of multipliers. A bound on the reconstruction of the noiseless received signal provides guidance on the selection of the relaxation parameter in the optimizations. The performance of the proposed estimation strategies are investigated via numerical simulations, and it is shown that the proposed non-convex method offers up to 7 dB improvement in low SNR and the convex method offers up to 5 dB improvement in high SNR over prior methods for the MSML channel estimation.

Description: Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications, agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lead to undesired results. In this paper, we propose an adaptive clustering and learning scheme that allows agents to learn which neighbors they should cooperate with and which other neighbors they should ignore. In doing so, the resulting algorithm enables the agents to identify their clusters and to attain improved learning and estimation accuracy over networks. We carry out a detailed mean-square analysis and assess the error probabilities of Types I and II, i.e., false alarm and misdetection, for the clustering mechanism. Among other results, we establish that these probabilities decay exponentially with the step-sizes so that the probability of correct clustering can be made arbitrarily close to one.

Description: Robust Chinese remainder theorem (CRT) has been recently investigated for both integers and real numbers, where the folding integers are accurately recovered from erroneous remainders. In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum-likelihood estimation (MLE) based CRT when the remainder noises may not necessarily have the same variances. Furthermore, we present a fast algorithm for the MLE based CRT algorithm that only needs to search for the solution among $L$ elements, where $L$ is the number of remainders. Then, a necessary and sufficient condition on the remainder errors for the MLE CRT to be robust is obtained, which is weaker than the existing result. Finally, we compare the performances of the newly proposed algorithm and the existing algorithm in terms of both theoretical analysis and numerical simulations. The results demonstrate that the proposed algorithm not only has a better performance especially when the remainders have different error levels/variances, but also has a much lower computational complexity.

Description: Sparse signal restoration is usually formulated as the minimization of a quadratic cost function $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}$ where $ { mbi { A}} $ is a dictionary and $ { mbi { x}} $ is an unknown sparse vector. It is well-known that imposing an $ell _{0}$ constraint leads to an NP-hard minimization problem. The convex relaxation approach has received considerable attention, where the $ell _{0}$ -norm is replaced by the $ell _{1}$ -norm. Among the many effective $ell _{1}$ solvers, the homotopy algorithm minimizes $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}+lambda Vert { mbi { x}} Vert _{1}$ with respect to $ { mbi { x}} $ for a continuum of $lambda $ ’s. It is inspired by the piecewise regularity of the $ell _{1}$ -regularization path, also referred to as the homotopy path. In this paper, we address the minimization problem $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}+lambda Vert { mbi { x}} Vert _{0}$ for a continuum of $lambda $ ’s and propose two heuristic search algorithms for $ell _{0}$ -homotopy. Continuation Single Best Replacement is a forward–backward greedy strategy extending the Single Best Replacement algorithm, previously proposed for $ell _{0}$ -minimization at a given $lambda $ . The adaptive search of the $lambda $ -values is inspired by $ell _{1}$ -homotopy. $ell _{0}$ Regularization Path Descent is a more complex algorithm exploiting the structural properties of the $ell _{0}$ -regularization path, which is piecewise constant with respect to $lambda $ . Both algorithms are empirically evaluated for difficult inverse problems involving ill-conditioned dictionaries. Finally, we show that they can be easily coupled with usual methods of model order selection.

Description: In recent work, robust Principal Components Analysis (PCA) has been posed as a problem of recovering a low-rank matrix ${bf L}$ and a sparse matrix ${bf S}$ from their sum, ${bf M}:= {bf L} + {bf S}$ and a provably exact convex optimization solution called PCP has been proposed. This work studies the following problem. Suppose that we have partial knowledge about the column space of the low rank matrix ${bf L}$ . Can we use this information to improve the PCP solution, i.e., allow recovery under weaker assumptions? We propose here a simple but useful modification of the PCP idea, called modified-PCP, that allows us to use this knowledge. We derive its correctness result which shows that, when the available subspace knowledge is accurate, modified-PCP indeed requires significantly weaker incoherence assumptions than PCP. Extensive simulations are also used to illustrate this. Comparisons with PCP and other existing work are shown for a stylized real application as well. Finally, we explain how this problem naturally occurs in many applications involving time series data, i.e., in what is called the online or recursive robust PCA problem. A corollary for this case is also given.

Description: The design and development of a plastic optical fiber macrobend temperature sensor is presented. The sensor can operate in a temperature range from −55 to 70 °C and has a linear response versus temperature with a sensitivity of 8.95·10 −4 °C −1 . The sensor system uses the ratio of transmittance at two wavelengths to implement a self-referencing technique in order to avoid undesirable power fluctuations influence. The transmittance ratio precision is 0.1%. An analysis has been developed to find the two wavelengths which ratio offers the highest linearity and sensitivity response. Experimental results are successfully compared with theoretical approaches.

Description: We review recent high capacity ultralong haul demonstrations using amplification schemes with extended C+L optical bandwidth and optimized nonlinear performance. These demonstrations achieve 49.3 Tb/s capacity over 9100 km with the use of C+L EDFAs and 54 Tb/s capacity over 9150 km using hybrid-Raman EDFAs. Using the same hybrid-Raman EDFAs, a capacity of 52.2 Tb/s over 10 230 km (534 Pb/s*km) was also demonstrated. Different combinations of three 16QAM-based coded modulation schemes with spectral efficiencies SE = 4.86, 5.4/5.45, 6.14 bit/s/Hz were used in order to maximize capacity for each amplification scheme and transmission distance. We also compare the merits of optimized transmission performance of hybrid Raman-EDFA and C+L EDFA amplification schemes based on our loop experiments. At the nonlinear limit, the use of hybrid Raman-EDFA provides a modest 9.5% increase in capacity at a cost of ∼2× higher electrical power.

Description: Flexible grid optical networks allow a better exploitation of fiber capacity, by enabling a denser frequency allocation. A tighter channel spacing, however, requires narrower filters, which increase linear intersymbol interference (ISI), and may dramatically reduce system reach. Commercial coherent receivers are based on symbol by symbol detectors, which are quite sensitive to ISI. In this context, Nyquist spacing is considered as the ultimate limit to wavelength-division multiplexing (WDM) packing. In this paper, we show that by employing a limited-complexity trellis processing at the receiver, either the reach of Nyquist WDM flexi-grid networks can be significantly extended, or a denser-than-Nyquist channel packing [i.e., a higher spectral efficiency (SE)] is possible at equal reach. By adopting well-known information-theoretic techniques, we design a limited-complexity trellis processing and quantify its SE gain in flexi-grid architectures where wavelength selective switches over a frequency grid of 12.5 GHz are employed.

Description: Phased array is widely used in radar systems with its beam steering fixed in one direction for all ranges. Therefore, the range of a target cannot be determined within a single pulse when range ambiguity exists. In this paper, an unambiguous approach for joint range and angle estimation is devised for multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA). Unlike the traditional phased array, FDA is capable of employing a small frequency increment across the array elements. Because of the frequency increment, the transmit steering vector of the FDA-MIMO radar is a function of both range and angle. As a result, the FDA-MIMO radar is able to utilize degrees-of-freedom in the range-angle domains to jointly determine the range and angle parameters of the target. In addition, the Cramér–Rao bounds for range and angle are derived, and the coupling between these two parameters is analyzed. Numerical results are presented to validate the effectiveness of the proposed approach.

Description: We analyze the shortest path lengths between node pairs of real optical transport networks. From the analysis, we find that Johnson ${rm S}_{B}$ distribution is suitable for the shortest path length modeling. The validity of the distributions is evaluated in terms of the Kolmogorov–Smirnov (KS) statistic. Johnson ${rm S}_{B}$ distribution provides an average KS statistic of 0.0423, which indicates its good accuracy. We also show that the key parameters of the shortest path lengths, such as the mean, the median, and the standard deviation, can be estimated from the convex area of the network. We develop the proposed Johnson ${rm S}_{B}$ distribution model for the shortest path lengths using the basic information of the networks. The developed model is able to estimate path-length dependent system parameters, such as the appropriate modulation formats in transparent optical networks with an average error of only $6.4{%}$ . It is noteworthy that these estimations can be made without full knowledge of the network. Only the node locations are required.

Description: We present a comprehensive numerical model for intrinsic small-signal modulation response of both reflective, and traveling-wave semiconductor optical amplifiers. We investigate the small-signal photon and carrier density spatial distribution, modulation response, and $-3text{dB}$ bandwidth for uniform and lossless traveling-wave modulation current model. The analysis shows that the current model does not significantly affect the modulation response as long as the modulation frequency is within the bandwidth. One of the most important results of our analysis is the discovery of the bandwidth maximum in case of a reflective semiconductor optical amplifier operating with low to moderate input optical powers and high current densities. The bandwidth can be further improved by choosing the optimal amplifier length.

Description: We study the effect of phase noise canceled polarization-insensitive all-optical wavelength conversion (AOWC) of orthogonal frequency-division multiplexing (OFDM) signals, which is based on four-wave-mixing in high-nonlinear optical fiber using dual-pump. An AOWC experiment of polarization division multiplexing OFDM (PDM-OFDM) 8/16/32-QAM signals is investigated. A DFB laser with linewidth of 10 MHz is employed as the coherent dual-pump seed. The measured results show that the received signal after wavelength conversion by utilizing coherent DFB dual-pump has the same BER performance as back-to-back. Furthermore, AOWC of 557-Gb/s superchannel discrete Fourier transform-spread PDM-OFDM with eight-QAM (DFT-S PDM-OFDM 8-QAM) signal is proposed and experimentally demonstrated based on the studied coherent dual-pump scheme. Negligible OSNR penalty (〈0.8 dB) is observed at 7% FEC limit ( ${rm BER} = 3.8 times 10^{-3}$ ) after wavelength conversion. Our demonstration shows that the phase noise transferred from the pumps can be effectively eliminated. It also shows that AOWC enables practical implementation on the polarization multiplexing dynamic optical networks at the nodes where wavelengths conflict.

Description: In this paper, we propose a novel single-group multicasting relay beamforming scheme. We assume a source that transmits common messages via multiple amplify-and-forward relays to multiple destinations. To increase the number of degrees of freedom in the beamforming design, the relays process two received signals jointly and transmit the Alamouti space-time block code over two different beams. Furthermore, in contrast to the existing relay multicasting scheme of the literature, we take into account the direct links from the source to the destinations. We aim to maximize the lowest received quality-of-service by choosing the proper relay weights and the ideal distribution of the power resources in the network. To solve the corresponding optimization problem, we propose an iterative algorithm which solves sequences of convex approximations of the original non-convex optimization problem. Simulation results demonstrate significant performance improvements of the proposed methods as compared with the existing relay multicasting scheme of the literature and an algorithm based on the popular semidefinite relaxation technique.

Description: In this paper, we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum-likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences generated by these algorithms to the set of fixed points of the maps that rule their dynamics and derive conditions verifiable a posteriori for the convergence to a sparse solution. We further prove that these algorithms are quadratically fast in a neighborhood of a sparse solution. We show through numerical experiments that the proposed methods outperform classical IRLS for $ell_{tau}$ -minimization with $tauin(0,1]$ in terms of speed and of sparsity-undersampling tradeoff and are robust even in presence of noise. The simplicity and the theoretical guarantees provided in this paper make this class of algorithms an attractive solution for sparse recovery problems.

Description: We consider the problem of approximating optimal in the Minimum Mean Squared Error (MMSE) sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised by a (possibly nonstationary) hidden stochastic process (the state), observed through another conditionally Gaussian stochastic process (the observations). Under general assumptions, we show that, given an approximating process which, for each time step, is stochastically convergent to the state process, an approximate filtering operator can be defined, which converges to the true optimal nonlinear filter of the state in a strong and well defined sense. In particular, the convergence is compact in time and uniform in a completely characterized set of probability measure almost unity. The results presented in this paper can form a common basis for the analysis and characterization of a number of popular but heuristic approaches for approximating optimal nonlinear filters, such as approximate grid based techniques.

Description: A standard assumption for consistent estimation in the errors-in-variables setting is persistency of excitation of the noise-free input signal. We relax this assumption by considering data from multiple experiments. Consistency is obtained asymptotically as the number of experiments tends to infinity. The main theoretical and algorithmic difficulties are related to the growing number of to-be-estimated initial conditions. The method proposed in the paper is based on analytic elimination of the initial conditions and optimization over the remaining parameters. The resulting estimator is consistent; however, achieving asymptotically efficiency is an open problem.

Description: Bayesian filtering aims at estimating sequentially a hidden process from an observed one. In particular, sequential Monte Carlo (SMC) techniques propagate in time weighted trajectories which represent the posterior probability density function (pdf) of the hidden process given the available observations. On the other hand, conditional Monte Carlo (CMC) is a variance reduction technique which replaces the estimator of a moment of interest by its conditional expectation given another variable. In this paper, we show that up to some adaptations, one can make use of the time recursive nature of SMC algorithms in order to propose natural temporal CMC estimators of some point estimates of the hidden process, which outperform the associated crude Monte Carlo (MC) estimator whatever the number of samples. We next show that our Bayesian CMC estimators can be computed exactly, or approximated efficiently, in some hidden Markov chain (HMC) models; in some jump Markov state-space systems (JMSS); as well as in multitarget filtering. Finally our algorithms are validated via simulations.

Description: In this paper, cooperative sensor localization using asynchronous time-of-arrival measurements is investigated. It is well known that localization performance in wireless networks using time-based ranging or pseudo-ranging methods is greatly affected by the accuracy of the timing synchronization between the nodes involved in the estimation. Commonly, the original estimation problem is broken down into two subproblems, the synchronization problem and the localization problem, in what is known as a two-step approach. However, in this paper, the joint synchronization and localization problem is considered and examined for use in cooperative networks. It is discussed that the cooperation between the source nodes eliminates the need for high anchor node densities and improves localization performance significantly. Furthermore, the Cramér-Rao lower bounds (CRLB) and the maximum likelihood (ML) estimator are derived. It is shown that the ML estimator is highly nonlinear and nonconvex and must, therefore, be solved by using computationally complex algorithms. In order to reduce the complexity of the estimation, a novel semidefinite programming (SDP) relaxation method is developed by relaxing the original nonconvex ML problem, in such a way as to reformulate the estimation problem as a convex problem. The performance of the proposed SDP method is shown through computer simulations to nearly equal that of the ML estimator. The approach is also applied to the noncooperative case where it is found to be superior in performance than the previously proposed suboptimal estimators. Finally, complexity analyses are included for the estimators under consideration.

Description: In this paper, we consider the problem of parameter estimation over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage distributed algorithm aiming at achieving the centralized sample mean estimate in a distributed manner. Different from the existing algorithms, a running average technique is utilized in the proposed algorithm to smear out the randomness caused by the probabilistic quantization scheme. With the running average technique, it is shown that the centralized sample mean estimate can be achieved both in the mean square and almost sure senses, which is not observed in the standard consensus algorithms. In addition, the rates of convergence are given to quantify the mean square and almost sure performances. Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm and highlight the improvements by using running average technique.

Description: This paper studies a pilot-assisted physical layer data fusion technique known as Distributed Co-Phasing (DCP). In this two-phase scheme, the sensors first estimate the channel to the fusion center (FC) using pilots sent by the latter; and then they simultaneously transmit their common data by pre-rotating them by the estimated channel phase, thereby achieving physical layer data fusion. First, by analyzing the symmetric mutual information of the system, it is shown that the use of higher order constellations (HOC) can improve the throughput of DCP compared to the binary signaling considered heretofore. Using an HOC in the DCP setting requires the estimation of the composite DCP channel at the FC for data decoding. To this end, two blind algorithms are proposed: 1) power method, and 2) modified $K$ -means algorithm. The latter algorithm is shown to be computationally efficient and converges significantly faster than the conventional $K$ -means algorithm. Analytical expressions for the probability of error are derived, and it is found that even at moderate to low SNRs, the modified $K$ -means algorithm achieves a probability of error comparable to that achievable with a perfect channel estimate at the FC, while requiring no pilot symbols to be transmitted from the sensor nodes. Also, the problem of signal corruption due to imperfect DCP is investigated, and constellation shaping to minimize the probability of signal corruption is proposed and analyzed. The analysis is validated, and the promising performance of DCP for energy-efficient physical layer data fusion is illustrated, using Monte Carlo simulations.

Description: In this paper we propose a fast and efficient Jacobi-like approach named JET (Joint Eigenvalue decomposition based on Triangular matrices) for the Joint EigenValue Decomposition (JEVD) of a set of real or complex non-defective matrices based on the LU factorization of the matrix of eigenvectors. Contrarily to classical Jacobi-like JEVD methods, the iterative procedure of the JET approach can be reduced to the search for only one of the two triangular matrices involved in the factorization of the matrix of eigenvectors, hence decreasing the numerical complexity. Two variants of the JET technique, namely JET-U and JET-O, which correspond to the optimization of two different cost functions are described in detail and these are extended to the complex case. Numerical simulations show that in many practical cases the JET approach provides more accurate estimation of the matrix of eigenvectors than its competitors and that the lowest numerical complexity is consistently achieved by the JET-U algorithm. In addition, we illustrate in the ICA context the interest of being able to solve efficiently the (non-orthogonal) JEVD problem. More particularly, based on our JET-U algorithm, we propose a more robust version of an existing ICA method, named MICAR-U. The identifiability of the latter is studied and proved under some conditions. Computer results given in the context of brain interfaces show the better ability of MICAR-U to denoise simulated electrocortical data compared to classical ICA techniques for low signal to noise ratio values.

Description: A new design for successive interference cancellation (SIC) detection for multiple-input multiple-output systems is introduced, and it is developed on the basis of the method of normal equations commonly used to solve the linear least squares problem. On the basis of this design, optimal-ordered and suboptimal-ordered SIC detection algorithms are derived. It is shown that the proposed optimal-ordered SIC detection algorithm offers a complexity reduction ratio of 1.11–1.25 compared to the fastest known optimal-ordered SIC detection algorithm for intermediate and large numbers of antennas and in terms of the average complexity. On the other hand, the proposed suboptimal-ordered SIC detection algorithm requires a lower complexity than the proposed optimal-ordered one and provides a bit-error-rate performance close to that of the optimal-ordered one and better than those of the other suboptimal-ordered SIC detection algorithms.

Description: We consider the problem of signal recovery on graphs. Graphs model data with complex structure as signals on a graph. Graph signal recovery recovers one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We formulate graph signal recovery as an optimization problem, for which we provide a general solution through the alternating direction methods of multipliers. We show how signal inpainting, matrix completion, robust principal component analysis, and anomaly detection all relate to graph signal recovery and provide corresponding specific solutions and theoretical analysis. We validate the proposed methods on real-world recovery problems, including online blog classification, bridge condition identification, temperature estimation, recommender system for jokes, and expert opinion combination of online blog classification.

Description: In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed $ell_1/ell_2$ -norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.

Description: Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification scheme in order to address the online learning paradigm. For this purpose, several online sparsification criteria have been proposed to restrict the model definition on a subset of samples. The most known criterion is the (linear) approximation criterion, which discards any sample that can be well represented by the already contributing samples, an operation with excessive computational complexity. Several computationally efficient sparsification criteria have been introduced in the literature with the distance and the coherence criteria. This paper provides a unified framework that connects these sparsification criteria in terms of approximating samples, by establishing theoretical bounds on the approximation errors. Furthermore, the error of approximating any pattern is investigated, by proposing upper bounds on the approximation error for each of the aforementioned sparsification criteria. Two classes of fundamental patterns are described in detail, the centroid (i.e., empirical mean) and the principal axes in the kernel principal component analysis. Experimental results show the relevance of the theoretical results established in this paper.

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Description: We report record 10 Gb/s bi-directional data transmission over a single 10 m SI-POF, by employing blue microlight-emitting diodes (μLEDs) at a single wavelength, APD receivers, and a PAM-32 modulation scheme. The implementation of 10 Gb/s LED-POF links takes advantage of the bi-directional configuration, which doubles the overall channel capacity, and APDs, which provide an enhanced link power budget owing to their improved sensitivity compared with conventional p-i-n photodiodes. Moreover, the high spectral efficiency of the PAM-32 modulation scheme employed, together with equalization techniques, enable the full utilization of the link bandwidth and the transmission of data rates higher than those obtained with conventional on–off keying. Simulation and experimental results demonstrate the feasibility of such a bi-directional link, and simultaneous 5 Gb/s data transmission is realized in each direction, achieving an aggregate data rate of 10 Gb/s with a BER 〈 10 −3 . The crosstalk penalty between the two directions of the link is measured to be less than 0.5 dB.

Description: Integrated polarization converters (PCs) with high (>99%) conversion efficiency open up many new possibilities in an InP-based photonic integrated circuit. In this paper, we describe how such a PC can be added to a circuit containing semiconductor optical amplifiers (SOAs), in order to obtain polarization independent amplification. Polarization independence is obtained by placing the PC halfway between two identical SOA sections. This approach has the advantage that no compromises in design and fabrication are needed. The polarization conversion is found to be very high, above 99.5%, when using a tolerant two-section PC. The extra insertion loss due to the converter is below 0.5 dB. The polarization-dependent gain (PDG) of the SOA reduces from 17 dB to only 0.3 dB by the inclusion of the PC. This is comparable to the best PDG values found in the literature with other techniques. The reduction is achieved over the whole C-band and for varying pump currents.

Description: We present the design, fabrication, and measurement results of low-insertion-loss and low-crosstalk broadband $2times 2$ Mach–Zehnder switches for nanosecond-scale optical data routing applications. We propose a simulation framework to calculate the spectral characteristics of switches and use it to design two switches: one based on directional couplers, the other using two-section directional couplers for broader bandwidth. We show that driving the switch in a push–pull manner enables to reduce insertion loss and optical crosstalk at the expense of the optical bandwidth. We achieve a good correlation between simulations and devices fabricated in IBM's 90-nm photonics-enabled CMOS process. We demonstrate a push–pull drive switch with insertion loss of $sim$ 1 dB and an optical crosstalk smaller than $-$ 23 dB over a 45-nm optical bandwidth in the O-band. We further achieve a transition time of $sim$ 4 ns with an average phase shifter consumption of 1 mW and a heater efficiency of $sim$ 25 mW $/pi$ .

Description: The optical spectra of composite one-dimensional photonic crystals (1-D PCs), fabricated by microstructuring of Si, are investigated. The composite PC is based on a Si-air structure and consists of two periodic 1-D PCs with various lattice constants a with $a_{1} = 2.7$ μm and $a_{2} = 4$ μm and various filling fractions f . The PCs are fabricated using photolithography followed by anisotropic chemical etching of (1 1 0) Si. Reflection and transmission spectra of this structure are measured using a Fourier transform infrared spectrometer combined with an IR microscope. The transmission spectra obtained demonstrate extended photonic stop bands (SBs), with characteristic transmission bands between the SBs. Theoretical and experimental transmission results indicate that the position of the extended SBs does not depend on the sequence of the individual PCs within the composite structure. In general, the calculated electric field distribution in the composite structure is similar to that of the individual PC components.

Description: We investigate five-channel polarization division multiplexed Nyquist WDM superchannel using offset-16QAM and 16QAM modulation format, under the condition of transmitter-side digital spectral shaping technique, respectively. The offset-16QAM scheme can greatly relax the implementation complexity, because the tap number of 35 is sufficient for the required finite impulse response (FIR) filter, compared with 81-tap FIR filter of the 16QAM scheme. About 1.3-dB back-to-back sensitivity improvement is obtained for the offset-16QAM scheme. In particular, the offset-16QAM scheme has better tolerance of DACs’ constraints, including the untracked jitter, sampling rate, and resolution. Finally, the practical implementation of offset-16QAM-based Nyquist WDM superchannel with TS-DSS is evaluated, by taking the phase difference deviation among wavelength channels and phase noise into account. No more than 1.3-dB required-OSNR penalty can be obtained, in case the phase difference is varied from 0° to 360°. When the laser with a linewidth of 100 kHz is used, there still exists about 1-dB overall performance improvement, compared with the 16QAM scheme.

Description: We investigate the optimum linewidth of the spectrum-sliced incoherent light (SSIL) source using a gain-saturated semiconductor optical amplifier (SOA) for the maximum capacity and longest transmission distance. For this purpose, we carry out experimental and simulation studies on the transmission performance of a 10-Gb/s on–off keying signal generated by using the SSIL source over a wide range of the SSIL linewidth. We find out that there are two windows of the linewidth for the high-speed operation of the SSIL source: ultra-narrow (i.e., linewidth $ll$ receiver bandwidth) and very wide (i.e., linewidth $gg$ receiver bandwidth). However, when the linewidth of the SSIL source is very wide, the 10-Gb/s signal generated by using this SSIL suffers severely from fiber chromatic dispersion and optical filtering. The simulation results are confirmed by experimental data measured by using an ultranarrow fiber Fabry–Perot filter (bandwidth = 700 MHz) and a bandwidth-tunable optical filter (bandwidth = 20 ∼ 53 GHz). Thus, we can conclude that the optimum linewidth of SSIL for capacity and transmission distance is ultranarrow. We also present a couple of drawbacks of the ultranarrow SSIL source, compared to the conventional wide-linewidth SSIL one, such as a large spectrum-slicing loss, a large SOA input power required for the suppression of excess intensity noise inherent in the incoherent light source, and the susceptibility to in-band crosstalk.

Description: The radix- $2^{k}$ algorithm plays a crucial role in the pipelined implementation of fast Fourier transform (FFT). This paper presents a fixed-point analysis and hardware evaluation of radix- $2^{k}$ FFT under the framework of the single-path delay feedback (SDF) and multi-path delay commutator (MDC) pipelined structure. The investigation is carried out with variable operating word-lengths to ensure the generality. Furthermore, the main streams to fulfill FFT coefficients weighting, namely, the approach using complex multipliers and the one adopting memoryless CORDIC units, are both considered in the analysis. Based on these derivations, a joint optimization of radix- $2^{k}$ algorithm and operating word-length is discussed to achieve a reasonable trade-off between computational accuracy and hardware expenditure. Simulations and experiments indicates that the derived SQNR is reliable to unfold the quantization effects of fixed-point radix- $2^{k}$ FFT. In addition, the proposed joint optimization strategy is capable of providing better solutions to implement the radix- $2^{k}$ FFT processor efficiently.

Description: The paper demonstrates a standardized process of vapor phase doping to fabricate large core Yb–doped preforms with longer useful length in reproducible manner. The optimization of the process led to successful achievement of Yb-doped core thickness of 4.5 mm (in 14.8 mm of preform diameter) by depositing up to 30 number of core layers with controlled amount of generated precursor vapors. The influence of the process parameters was studied rigorously to enhance the useful preform length up to 380 mm. A combination of Yb and Al in different proportions was doped into the core with uniform dopant concentration along the length by adjusting few process parameters efficiently. The Al 2 O 3 concentration up to the level of 17.8 mol% has been achieved successfully which resulted in NA of 0.31. This is the highest ever doping of Al in passive fibers by any modified chemical vapor deposition process. The Yb 2 O 3 content in the active fibers is as high as 0.47 mol%.

Description: In this paper, the quickest change detection problem is studied in two-state hidden Markov models (HMM), where the vector parameter $theta$ of the HMM changes from $theta_{0}$ to $theta_{1}$ at some unknown time, and one wants to detect the true change as quickly as possible while controlling the false alarm rate. It turns out that the generalized likelihood ratio (GLR) scheme, while theoretically straightforward, is generally computationally infeasible for the HMM. To develop efficient but computationally simple schemes for the HMM, we first discuss a subtlety in the recursive form of the generalized likelihood ratio (GLR) scheme for the HMM. Then we show that the recursive CUSUM scheme proposed in Fuh (Ann. Statist., 2003) can be regarded as a quasi-GLR scheme for pseudo post-change hypotheses with certain dependence structure between pre- and postchange observations. Next, we extend the quasi-GLR idea to propose recursive score schemes in the scenario when the postchange parameter $theta_{1}$ of the HMM involves a real-valued nuisance parameter. Finally, the Kullback-Leibler (KL) divergence plays an essential role in the quickest change detection problem and many other fields, however it is rather challenging to numerically compute it in HMMs. Here we develop a non-Monte Carlo method that computes the KL divergence of two-state HMMs via the underlying invariant probability measure, which is characterized by the Fredholm integral equation. Numerical study demonstrates an unusual property of the KL divergence for HMM that implies the severe effects of misspecifying the postchange parameter for the HMM.

Description: Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

Description: In view of the increasing frequency and damage severity of disasters, network operators have become more concerned with providing disaster-resiliency measures for their optical network infrastructure, whereas mitigating network service interruption due to the disaster region failures in the optical physical medium merely by increasing network redundancy is deemed spatially inefficient and very costly, with recent advancements, wireless technology is a potential candidate solution for efficient medium diversification. This paper addresses the challenge of efficiently designing disaster-resilient wireless-link-augmented optical network infrastructure. We formulate this problem as an optimization model of finding the subset of links in an optical network topology whose wireless augmentation maximizes postdisaster recovery of overall network availability for a given budget constraint. To overcome the computational complexity of finding the optimal design solution, a novel greedy heuristic algorithm is proposed. Performance comparisons with an exhaustive enumeration search and simple heuristics demonstrate the efficiency and scalability of our heuristic algorithm.

Description: In order to characterize the channel capacity of a wavelength channel in a wavelength-division multiplexed (WDM) system, statistical models are needed for the transmitted signals on the other wavelengths. For example, one could assume that the transmitters for all wavelengths are configured independently of each other, that they use the same signal power, or that they use the same modulation format. In this paper, it is shown that these so-called behavioral models have a profound impact on the single-wavelength achievable information rate. This is demonstrated by establishing, for the first time, upper and lower bounds on the maximum achievable rate under various behavioral models, for a rudimentary WDM channel model.

Description: The discrete cosine transform (DCT) is known to be asymptotically equivalent to the Karhunen-Loève transform (KLT) of Gaussian first-order auto-regressive (AR(1)) processes. Since being uncorrelated under the Gaussian hypothesis is synonymous with independence, it also yields an independent-component analysis (ICA) of such signals. In this paper, we present a constructive non-Gaussian generalization of this result: the characterization of the optimal orthogonal transform (ICA) for the family of symmetric- $alpha$ -stable AR(1) processes. The degree of sparsity of these processes is controlled by the stability parameter $0 〈 alphaleq2$ with the only non-sparse member of the family being the classical Gaussian AR(1) process with $alpha=2$ . Specifically, we prove that, for $alpha 〈 2$ , a fixed family of operator-like wavelet bases systematically outperforms the DCT in terms of compression and denoising ability. The effect is quantified with the help of two performance criteria (one based on the Kullback-Leibler divergence, and the other on Stein’s formula for the minimum estimation error) that can also be viewed as statistical measures of independence. Finally, we observe that, for the sparser kind of processes with $0 〈 alphaleq 1$ , the operator-like wavelet basis, as dictated by linear system theory, is undistinguishable from the ICA solution obtained through numerical optimization. Our framework offers a unified view that encompasses sinusoidal transforms such as the DCT and a family of orthogonal Haar-like wavelets that is linked analytically to the underlying signal model.

Description: MUSIC is a popular algorithm for estimating the direction of arrival (DOA) in array signal processing applications. In this paper, we analyze the performance of the MUSIC algorithm for a single source system, in the presence of noisy and missing data (when only a random subset of the entries in the data matrix are observed). We prove consistency of the DOA estimate when signal from a single source is impinging on low coherence arrays, and derive an analytic expression for the mean-squared-error (MSE) performance of MUSIC for the case of uniform linear arrays, in the large array and relatively large sample setting. Our analysis is mathematically justified in both the sample rich and deficient regimes. The expression for the MSE is a simple function of array geometry, signal-to-noise ratio (SNR), the fraction of entries observed, and the ratio of the number of sensors to number of snapshots. We derive a phase transition threshold which separates a regime where MUSIC is consistent from a regime where MUSIC is inconsistent. This threshold depends upon the SNR, the probability of observing entries in the data matrix, and number of sensors and snapshots in a simple manner which we make explicit.

Description: Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. More than four decades after it was first proposed, the seminal error reduction algorithm of Gerchberg and Saxton and Fienup is still the popular choice for solving many variants of this problem. The algorithm is based on alternating minimization; i.e., it alternates between estimating the missing phase information, and the candidate solution. Despite its wide usage in practice, no global convergence guarantees for this algorithm are known. In this paper, we show that a (resampling) variant of this approach converges geometrically to the solution of one such problem—finding a vector $bf x$ from ${bf y}, {bf A}$ , where ${bf y} = vert {bf A}^T{bf x}vert$ and $vert{bf z}vert$ denotes a vector of element-wise magnitudes of ${bf z}$ —under the assumption that $ {bf A}$ is Gaussian. Empirically, we demonstrate that alternating minimization performs similar to recently proposed convex techniques for this problem (which are based on “lifting” to a convex matrix problem) in sample complexity and robustness to noise. However, it is much more efficient and can scale to large problems. Analytically, for a resampling version of alternating minimization, we show geometric convergence to the solution, and sample complexity that is off by log factors from obvious lower bounds. We also establish close to optimal scaling for the case when the unknown vector is sparse. Our work represents the first theoretical guarantee for al- ernating minimization (albeit with resampling) for any variant of phase retrieval problems in the non-convex setting.

Description: We consider Bayesian blind deconvolution (BD) of an unknown sparse sequence convolved with an unknown pulse. Our goal is to detect the positions where the sparse input sequence is nonzero and to estimate the corresponding amplitudes as well as the pulse shape. For this task, we propose a novel evolution of the single most likely replacement (SMLR) algorithm. Our method uses a modified Bernoulli-Gaussian prior that incorporates a minimum temporal distance constraint. This prior simultaneously induces sparsity and enforces a prescribed minimum distance between the pulse centers. The minimum distance constraint provides an effective way to avoid overfitting (i.e., spurious detected pulses) and improve resolution. The proposed BD method overcomes certain weaknesses of the traditional SMLR-based BD method, which is verified experimentally to result in improved detection/estimation performance and reduced computational complexity. Our simulation results also demonstrate performance and complexity advantages relative to the iterated window maximization (IWM) algorithm and a recently proposed partially collapsed Gibbs sampler method.

Description: We address the problem of super-resolution frequency recovery using prior knowledge of the structure of a spectrally sparse, undersampled signal. In many applications of interest, some structure information about the signal spectrum is often known. The prior information might be simply knowing precisely some signal frequencies or the likelihood of a particular frequency component in the signal. We devise a general semidefinite program to recover these frequencies using theories of positive trigonometric polynomials. Our theoretical analysis shows that, given sufficient prior information, perfect signal reconstruction is possible using signal samples no more than thrice the number of signal frequencies. Numerical experiments demonstrate great performance enhancements using our method. We show that the nominal resolution necessary for the grid-free results can be improved if prior information is suitably employed.

Description: This paper addresses the behavior of a classical multiantenna GLRT test that allows to detect the presence of a known signal corrupted by a multipath propagation channel and by an additive temporally white Gaussian noise with unknown spatial covariance matrix. The paper is focused on the case where the number of sensors $M$ is large, and of the same order of magnitude as the sample size $N$ , a context which is modeled by the large system asymptotic regime $M rightarrow +infty $ , $N rightarrow +infty $ in such a way that $M/N rightarrow c$ for $c in (0,+infty )$ . The purpose of this paper is to study the behaviour of a GLRT statistics in this regime, and to show that the corresponding theoretical analysis allows to accurately predict the performance of the test when $M$ and $N$ are of the same order of magnitude.

Description: This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman’s theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loève spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.

Description: The impulse response of wireless channels between the $N_t$ transmit and $N_r$ receive antennas of a MIMO-OFDM system are group approximately sparse (ga-sparse), i.e., the $N_tN_r$ channels have a small number of significant paths relative to the channel delay spread and the time-lags of the significant paths between transmit and receive antenna pairs coincide. Often, wireless channels are also group approximately cluster-sparse (gac-sparse), i.e., every ga-sparse channel consists of clusters, where a few clusters have all strong components while most clusters have all weak components. In this paper, we cast the problem of estimating the ga-sparse and gac-sparse block-fading and time-varying channels in the sparse Bayesian learning (SBL) framework and propose a bouquet of novel algorithms for pilot-based channel estimation, and joint channel estimation and data detection, in MIMO-OFDM systems. The proposed algorithms are capable of estimating the sparse wireless channels even when the measurement matrix is only partially known. Further, we employ a first-order autoregressive modeling of the temporal variation of the ga-sparse and gac-sparse channels and propose a recursive Kalman filtering and smoothing (KFS) technique for joint channel estimation, tracking, and data detection. We also propose novel, parallel-implementation based, low-complexity techniques for estimating gac-sparse channels. Monte Carlo simulations illustrate the benefit of exploiting the gac-sparse structure in the wireless channel in terms of the mean square error (MSE) and coded bit error rate (BER) performance.

Description: One-dimensional (1-D) and two-dimensional (2-D) frequency estimation for a single complex sinusoid in white Gaussian noise is a classic signal processing problem with numerous applications. It is revisited here through a new unitary principal-singular-vector utilization modal analysis (PUMA) approach, which is realized in terms of real-valued computations. The 2-D unitary PUMA is first formulated as an iteratively weighted least squares optimization problem. Recognizing that only one iteration is sufficient when 2-D unitary PUMA is initialized using least squares, a computationally attractive closed-form solution is then obtained. A variant of 2-D unitary PUMA is also developed for the 1-D case. Due to the real-valued computations and closed-form expression for the frequency estimate, the unitary PUMA is more computationally efficient than a number of state-of-the-art methods. Furthermore, the asymptotic variances of 1-D and 2-D unitary PUMA estimators are theoretically derived, and numerical results are included to demonstrate the effectiveness of the proposed methods.

Description: Distributed estimation over sensor networks has received a lot of attention due to its great promise for broad applicability. In many cases, sensors have constraints on the range of data they can measure. This may cause that the measurements or observations are censored, and hence the value of a measurement or observation could be only partially known. This paper focuses on distributed censored regression over networks and develops a diffusion-based algorithm for the censored regression. The proposed algorithm first adopts an adaptive bias-corrected estimator based on a probit regression model to reduce the adverse effect of censoring on estimation results, and afterwards carries out the least squares procedure to find the estimate of the parameter of interest in a collaborative manner between every node and its neighbors. The theoretical study of convergence in the mean and mean-square sense reveals that the proposed algorithm is asymptotically unbiased and stable under some conditions. Moreover, simulation results show the effectiveness of the proposed algorithm.

Description: Computational load remains a major concern when processing signals by means of sliding transforms. In this paper, we present an efficient algorithm for the fast computation of one-dimensional and two-dimensional sliding discrete Tchebichef moments. To do so, we first establish the relationships that exist between the Tchebichef moments of two neighboring windows taking advantage of Tchebichef polynomials’ properties. We then propose an original way to fast compute the moments of one window by utilizing the moment values of its previous window. We further theoretically establish the complexity of our fast algorithm and illustrate its interest within the framework of digital forensics and more precisely the detection of duplicated regions in an audio signal or an image. Our algorithm is used to extract local features of such a signal tampering. Experimental results show that its complexity is independent of the window size, validating the theory. They also exhibit that our algorithm is suitable to digital forensics and beyond to any applications based on sliding Tchebichef moments.

Description: In this paper, we describe a model for maximum likelihood estimation (MLE) of the relative abundances of different conformations of a protein in a heterogeneous mixture from small angle X-ray scattering (SAXS) intensities. To consider cases where the solution includes intermediate or unknown conformations, we develop a subset selection method based on k-means clustering and the Cramér-Rao bound on the mixture coefficient estimation error to find a sparse basis set that represents the space spanned by the measured SAXS intensities of the known conformations of a protein. Then, using the selected basis set and the assumptions on the model for the intensity measurements, we show that the MLE model can be expressed as a constrained convex optimization problem. Employing the adenylate kinase (ADK) protein and its known conformations as an example, and using Monte Carlo simulations, we demonstrate the performance of the proposed estimation scheme. Here, although we use 45 crystallographically determined experimental structures and we could generate many more using, for instance, molecular dynamics calculations, the clustering technique indicates that the data cannot support the determination of relative abundances for more than 5 conformations. The estimation of this maximum number of conformations is intrinsic to the methodology we have used here.

Description: Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory computation on a single machine; and the few that break away from this mold do not easily incorporate practically important constraints, such as non-negativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction Method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration; and it naturally leads to distributed algorithms suitable for parallel implementation. This opens the door for many emerging big data-enabled applications. The methodology is exemplified using non-negativity as a baseline constraint, but the proposed framework can incorporate many other types of constraints. Numerical experiments are encouraging, indicating that ADMoM-based non-negative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.

Description: A method for authorship attribution based on function word adjacency networks (WANs) is introduced. Function words are parts of speech that express grammatical relationships between other words but do not carry lexical meaning on their own. In the WANs in this paper, nodes are function words and directed edges from a source function word to a target function word stand in for the likelihood of finding the latter in the ordered vicinity of the former. WANs of different authors can be interpreted as transition probabilities of a Markov chain and are therefore compared in terms of their relative entropies. Optimal selection of WAN parameters is studied and attribution accuracy is benchmarked across a diverse pool of authors and varying text lengths. This analysis shows that, since function words are independent of content, their use tends to be specific to an author and that the relational data captured by function WANs is a good summary of stylometric fingerprints. Attribution accuracy is observed to exceed the one achieved by methods that rely on word frequencies alone. Further combining WANs with methods that rely on word frequencies, results in larger attribution accuracy, indicating that both sources of information encode different aspects of authorial styles.

Description: Greed is good. However, the tighter you squeeze, the less you have. In this paper, a less greedy algorithm for sparse signal reconstruction in compressive sensing, named orthogonal matching pursuit with thresholding is studied. Using the global 2-coherence, which provides a “bridge” between the well known mutual coherence and the restricted isometry constant, the performance of orthogonal matching pursuit with thresholding is analyzed and more general results for sparse signal reconstruction are obtained. It is also shown that given the same assumption on the coherence index and the restricted isometry constant as required for orthogonal matching pursuit, the thresholding variation gives exactly the same reconstruction performance with significantly less complexity.

Description: In multiobject inference, the multiobject probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multiobject density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multiobject density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multiobject distribution of interest. It is also shown that the proposed approximation minimizes the Kullback–Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multiobject tracking algorithm for generic measurement models. Simulation results for a multiobject Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.

Description: With bandwidths on the order of a gigahertz in emerging wireless systems, high-resolution analog-to-digital convertors (ADCs) become a power consumption bottleneck. One solution is to employ low resolution one-bit ADCs. In this paper, we analyze the flat fading multiple-input multiple-output (MIMO) channel with one-bit ADCs. Channel state information is assumed to be known at both the transmitter and receiver. For the multiple-input single-output channel, we derive the exact channel capacity. For the single-input multiple-output and MIMO channel, the capacity at infinite signal-to-noise ratio (SNR) is found. We also derive upper bound at finite SNR, which is tight when the channel has full row rank. In addition, we propose an efficient method to design the input symbols to approach the capacity achieving solution. We incorporate millimeter wave channel characteristics and find the bounds on the infinite SNR capacity. The results show how the number of paths and number of receive antennas impact the capacity.

Description: Based on the biorthogonal analysis approach, a multiwindow real-valued discrete Gabor transform (M-RDGT) for periodic sequences is presented to efficiently analyze the dynamic time-frequency content of a signal containing components with multiple and/or time-varying frequencies. The M-RDGT offers a computationally efficient implementation as well as a real-valued formulation of the multiwindow complex-valued discrete Gabor transform (M-CDGT). The completeness condition of the M-RDGT is proved to be equivalent to its biorthogonality constraint between analysis windows and synthesis windows. The M-RDGT can utilize the fast discrete Hartley transform algorithms for fast computation and has a simple relationship with the M-CDGT such that its coefficients can be directly computed from the M-RDGT coefficients. Therefore, the M-RDGT offers an efficient method to compute the M-CDGT. Since the analyzed sequence, analysis and synthesis windows in the existing M-CDGT must have an equal period, if the period of a sequence is very long, solving its windows requires a huge amount of computation and memory and could lead to numerical instability. To overcome this problem, a modified M-RDGT for long-periodic (or even infinite) sequences is presented and its corresponding biorthogonality constraint between analysis windows and synthesis windows is modified, in which the period of the analysis and synthesis windows is independent of the period of a analyzed sequence so that one can apply short windows to process any long-periodic (or even in finite) sequence. Finally, the multirate-based parallel implementation of the M-RDGT is presented, which has shown to be effective and fast for time-frequency analysis.

Description: In this paper, we present a Bayesian approach for spectral unmixing of multispectral Lidar (MSL) data associated with surface reflection from targeted surfaces composed of several known materials. The problem addressed is the estimation of the positions and area distribution of each material. In the Bayesian framework, appropriate prior distributions are assigned to the unknown model parameters and a Markov chain Monte Carlo method is used to sample the resulting posterior distribution. The performance of the proposed algorithm is evaluated using synthetic MSL signals, for which single and multi-layered models are derived. To evaluate the expected estimation performance associated with MSL signal analysis, a Cramer-Rao lower bound associated with model considered is also derived, and compared with the experimental data. Both the theoretical lower bound and the experimental analysis will be of primary assistance in future instrument design.

Description: This paper demonstrates the feasibility of layered space-time coding (STC) in an outdoor image-sensor-based (IS-based) visible light communication (VLC) system. We examined that for low-resolution IS-based VLC channel where intensity-modulated signals from two different light emitting diodes (LEDs) are detected by one pixel of an IS, STC allows us to decouple them; thus, succeeding to receive them with no errors. Consequently, STC offers extended transmission distance to pixel-resolution-limited IS-based VLC links. In the layered STC presented in this paper, additional bit streams are laid on the ${2^ntimes 2^n}$  LED array for increasing the transmission rate per symbol duration for the case where the pixel resolution is improved. A prototype of a three-layered STC is built with an ${8times 8}$  LED array, where each of the LEDs is modulated at 1 kb/s and a high-speed camera with IS operating at 1000 fps. Our experimental results validate that the two additional bit streams (layer-2 and -3), aligned in the layer-1 STC matrix pair, are extracted with no errors when the receiver comes within 155 and 55 m, respectively, from the LED array, without decreasing 210 m of the transmission distance of layer-1 bit stream.

Description: We present simulation and experimental results on a silicon photonic strictly nonblocking $4times 4$ electrooptic Mach–Zehnder-based switch fabric. We propose a simulation framework based on the transfer matrix approach that enables calculating the transmission spectra of any type of multistage interconnect switch network. The model is used to analyze the spectral characteristics of the switch fabric. We also show experimental results on a fabric designed and fabricated in IBM's 90-nm photonics-enabled CMOS process. The fabric monolithically integrates the CMOS logic, the switch drivers, and all the photonics. We fully characterized all the transmittances of the switch and demonstrate onchip insertion loss between 1.5 and 3 dB and a crosstalk less than –25 dB for all the signal paths.

Description: The FEC limit paradigm is the prevalent practice for designing optical communication systems to attain a certain bit error rate (BER) without forward error correction (FEC). This practice assumes that there is an FEC code that will reduce the BER after decoding to the desired level. In this paper, we challenge this practice and show that the concept of a channel-independent FEC limit is invalid for soft-decision bit-wise decoding. It is shown that for low code rates and high-order modulation formats, the use of the soft-decision FEC limit paradigm can underestimate the spectral efficiencies by up to 20%. A better predictor for the BER after decoding is the generalized mutual information, which is shown to give consistent post-FEC BER predictions across different channel conditions and modulation formats. Extensive optical full-field simulations and experiments are carried out in both the linear and nonlinear transmission regimes to confirm the theoretical analysis.

Description: We propose an analytical time-domain model for microring and microdisk modulators, which considers both their electrical and optical properties. Theory of the dynamics of microring/microdisk is discussed, and general solutions to the transfer matrix representation are presented. Both static and dynamic predictions from the model are compared to measurement results to demonstrate the accuracy of our model. Static predictions and measurements are presented for power and phase responses, whereas dynamic predictions and measurements are presented for small-signal and large-signal operations. The model verifies that the chirping and modulation bandwidth of the modulators depend on the detuning state. Finally, the accuracy and scalability of several techniques employed in the model are discussed.

Description: We experimentally investigate an improved fast orthogonal frequency division multiplexing (OFDM) scheme using intensity modulation and full-field detection. This new fast OFDM algorithm exhibits better back-to-back and transmission performance than the conventional one, and is shown to support a 40-Gb/s signal over a 480-km re-circulating loop-based single-mode fiber (SMF) with 1-dB penalty. We compare this scheme with the direct-detection (DD) system using the same algorithm, and show that the DD system cannot support 60-km SMF at 40 Gb/s. Finally, we demonstrate the proposed scheme over 124 km of BT Ireland's field-installed fiber without inline optical amplification. The results show that this scheme can be a promising solution to address the gap between direct detection and coherent detection for applications in short metro networks and long-reach Ethernet and access networks.

Description: We describe branched-fiber sensing with a novel Brillouin time-domain analysis configuration for temperature or strain sensing within a 2-D expanded area. By using a branch configuration with an appropriate number of branches and the newly proposed multiprobe pulse arrangement, the system dynamic range may surpass that of the conventional Brillouin time-domain analyzer, which employs a unicursal fiber configuration. The proposed Brillouin sensing would be useful particularly when the sensed area is 2-D, rather than when the area expands 1-D.

Description: The boundaryless beam propagation method has been reported to suffer from reflections due to frequency aliasing. We propose an alternate explanation of reflection in the method, based on eikonal analysis of the wave equation in the mapped space, and show that reflection starts much before aliasing happens. We theoretically predict the reflection coefficient profile in a simulation and introduce an internal absorbing boundary condition (ABC), where reflection becomes appreciable. The a priori knowledge of the window size of the ABC does away with the arbitrariness in the design of such boundaries. Finally, we use the boundaryless scheme with the ABC to successfully model a dielectric bend.

Description: This study presents a photonic integrated transceiver for application in a data readout unit of a sensor network. The device was realized in a generic InP-based technology. The circuit uses an asymmetric coupler and a PIN photodiode for input signal detection and a Mach–Zehnder amplitude modulator for encoding of output sensor data. Small-signal modulation bandwidth of 11.1 GHz was measured, eye-diagrams with a dynamic extinction ratio of 11 dB were recorded, and transmission of a 10-Gb/s signal over 25 km of SMF fiber with bit error rate below 10 −10 was achieved.