By Topic

Signal Processing, IEEE Transactions on

Issue 10 • Date May15, 2013

Filter Results

Displaying Results 1 - 25 of 35
  • [Front cover]

    Publication Year: 2013 , Page(s): C1
    Save to Project icon | Request Permissions | PDF file iconPDF (289 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Signal Processing publication information

    Publication Year: 2013 , Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (135 KB)  
    Freely Available from IEEE
  • Table of Contents

    Publication Year: 2013 , Page(s): 2425 - 2426
    Save to Project icon | Request Permissions | PDF file iconPDF (231 KB)  
    Freely Available from IEEE
  • Table of Contents

    Publication Year: 2013 , Page(s): 2427 - 2428
    Save to Project icon | Request Permissions | PDF file iconPDF (232 KB)  
    Freely Available from IEEE
  • Low-Latency Sequential and Overlapped Architectures for Successive Cancellation Polar Decoder

    Publication Year: 2013 , Page(s): 2429 - 2441
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3082 KB) |  | HTML iconHTML  

    Polar codes have recently emerged as one of the most favorable capacityachieving error correction codes due to their low encoding and decoding complexity. However, because of the large code length required by practical applications, the few existing successive cancellation (SC) decoder implementations still suffer from not only high hardware cost but also long decoding latency. In this paper, a data-flow graph (DFG) for the SC decoder is derived. A complete hardware architecture is first derived for the conventional tree SC decoder and the feedback part is presented next. Precomputation look-ahead technique is exploited to reduce the achievable minimum decoding latency. Substructure sharing is used to design a merged processing element (PE) for higher hardware utilization. In order to meet throughput requirements for a diverse set of application scenarios, a systematic approach to construct different overlapped SC polar decoder architectures is also presented. Compared with a conventional N -bit tree SC decoder, the proposed overlapped architectures can achieve as high as (N-1) times speedup with only ({N}\log _{2}{N})/2 merged PEs. The proposed pre-computation approach leads to a 50% reduction in latency for {N} > 2^{7} , and 40% reduction for N \leq 2^{7} . View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Nonlinear Spectral Unmixing of Hyperspectral Images Using Gaussian Processes

    Publication Year: 2013 , Page(s): 2442 - 2453
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3291 KB) |  | HTML iconHTML  

    This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components. We assume that the spectral signatures of the pure components and the nonlinear function are unknown. The first step of the proposed method estimates the abundance vectors for all the image pixels using a Bayesian approach an a Gaussian process latent variable model for the nonlinear function (relating the abundance vectors to the observations). The endmembers are subsequently estimated using Gaussian process regression. The performance of the unmixing strategy is first evaluated on synthetic data. The proposed method provides accurate abundance and endmember estimations when compared to other linear and nonlinear unmixing strategies. An interesting property is its robustness to the absence of pure pixels in the image. The analysis of a real hyperspectral image shows results that are in good agreement with state of the art unmixing strategies and with a recent classification method. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Noncooperative and Cooperative Optimization of Distributed Energy Generation and Storage in the Demand-Side of the Smart Grid

    Publication Year: 2013 , Page(s): 2454 - 2472
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5296 KB) |  | HTML iconHTML  

    The electric energy distribution infrastructure is undergoing a startling technological evolution with the development of the smart grid concept, which allows more interaction between the supply- and the demand-side of the network and results in a great optimization potential. In this paper, we focus on a smart grid in which the demand-side comprises traditional users as well as users owning some kind of distributed energy source and/or energy storage device. By means of a day-ahead demand-side management mechanism regulated through an independent central unit, the latter users are interested in reducing their monetary expense by producing or storing energy rather than just purchasing their energy needs from the grid. Using a general energy pricing model, we tackle the grid optimization design from two different perspectives: a user-oriented optimization and an holistic-based design. In the former case, we optimize each user individually by formulating the grid optimization problem as a noncooperative game, whose solution analysis is addressed building on the theory of variational inequalities. In the latter case, we focus instead on the joint optimization of the whole system, allowing some cooperation among the users. For both formulations, we devise distributed and iterative algorithms providing the optimal production/storage strategies of the users, along with their convergence properties. Among all, the proposed algorithms preserve the users' privacy and require very limited signaling with the central unit. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • On U-Statistics and Compressed Sensing I: Non-Asymptotic Average-Case Analysis

    Publication Year: 2013 , Page(s): 2473 - 2485
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4704 KB) |  | HTML iconHTML  

    Hoeffding's U-statistics model combinatorial-type matrix parameters (appearing in CS theory) in a natural way. This paper proposes using these statistics for analyzing random compressed sensing matrices, in the non-asymptotic regime (relevant to practice). The aim is to address certain pessimisms of "worst-case" restricted isometry analyses, as observed by both Blanchard & Dossal, et. al. We show how U-statistics can obtain "average-case" analyses, by relating to statistical restricted isometry property (StRIP) type recovery guarantees. However unlike standard StRIP, random signal models are not required; the analysis here holds in the almost sure (probabilistic) sense. For Gaussian/bounded entry matrices, we show that both ℓ1-minimization and LASSO essentially require on the order of k · [log((n-k)/u) + √(2(k/n) log(n/k))] measurements to respectively recover at least 1-5u fraction, and 1-4u fraction, of the signals. Noisy conditions are considered. Empirical evidence suggests our analysis to compare well to Donoho & Tanner's recent large deviation bounds for ℓ0/ℓ1-equivalence, in the regime of block lengths 1000~3000 with high undersampling (50-150 measurements); similar system sizes are found in recent CS implementation. In this work, it is assumed throughout that matrix columns are independently sampled. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • On U-Statistics and Compressed Sensing II: Non-Asymptotic Worst-Case Analysis

    Publication Year: 2013 , Page(s): 2486 - 2497
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4908 KB) |  | HTML iconHTML  

    In another related work, U-statistics were used for non-asymptotic average-case analysis of random compressed sensing matrices. In this companion paper the same analytical tool is adopted differently-here we perform non-asymptotic worst-case analysis. Simple union bounds are a natural choice for worst-case analyses, however their tightness is an issue (and questioned in previous works). Here we focus on a theoretical U-statistical result, which potentially allows us to prove that these union bounds are tight. To our knowledge, this kind of (powerful) result is completely new in the context of CS. This general result applies to a wide variety of parameters, and is related to (Stein-Chen) Poisson approximation. In this paper, we consider i) restricted isometries, and ii) mutual coherence. For the bounded case, we show that -th order restricted isometry constants have tight union bounds, when the measurements m = O (k(1.5(+ log(n/k))). Here, we require the restricted isometries to grow linearly in , however we conjecture that this result can be improved to allow them to be fixed. Also, we show that mutual coherence (with the standard estimate √(4 log n)/m) have very tight union bounds. For coherence, the normalization complicates general discussion, and we consider only Gaussian and Bernoulli cases here. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Social Sparsity! Neighborhood Systems Enrich Structured Shrinkage Operators

    Publication Year: 2013 , Page(s): 2498 - 2511
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3895 KB) |  | HTML iconHTML  

    Sparse and structured signal expansions on dictionaries can be obtained through explicit modeling in the coefficient domain. The originality of the present article lies in the construction and the study of generalized shrinkage operators, whose goal is to identify structured significance maps and give rise to structured thresholding. These generalize Group-Lasso and the previously introduced Elitist Lasso by introducing more flexibility in the coefficient domain modeling, and lead to the notion of social sparsity. The proposed operators are studied theoretically and embedded in iterative thresholding algorithms. Moreover, a link between these operators and a convex functional is established. Numerical studies on both simulated and real signals confirm the benefits of such an approach. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Weighted Sum Rate Maximization for Downlink OFDMA With Subcarrier-Pair Based Opportunistic DF Relaying

    Publication Year: 2013 , Page(s): 2512 - 2524
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3332 KB) |  | HTML iconHTML  

    This paper addresses a weighted sum rate (WSR) maximization problem for downlink OFDMA aided by a decode-and-forward (DF) relay under a total power constraint. A novel subcarrier-pair based opportunistic DF relaying protocol is proposed. Specifically, user message bits are transmitted in two time slots. A subcarrier in the first slot can be paired with a subcarrier in the second slot for the DF relay-aided transmission to a user. In particular, the source and the relay can transmit simultaneously to implement beamforming at the subcarrier in the second slot. Each unpaired subcarrier in either the first or second slot is used for the source's direct transmission to a user. A benchmark protocol, same as the proposed one except that the transmit beamforming is not used for the relay-aided transmission, is also considered. For each protocol, a polynomial-complexity algorithm is developed to find at least an approximately optimum resource allocation (RA), by using continuous relaxation, the dual method, and Hungarian algorithm. Instrumental to the algorithm design is an elegant definition of optimization variables, motivated by the idea of regarding the unpaired subcarriers as virtual subcarrier pairs in the direct transmission mode. The effectiveness of the RA algorithm and the impact of relay position and total power on the protocols' performance are illustrated by numerical experiments. It is shown that for each protocol, it is more likely to pair subcarriers for relay-aided transmission when the total power is low and the relay lies in the middle between the source and user region. The proposed protocol always leads to a maximum WSR equal to or greater than that for the benchmark one, and the performance gain of using the proposed one is significant especially when the relay is in close proximity to the source and the total power is low. Theoretical analysis is presented to interpret these observations. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Schedule Communication for Decentralized State Estimation

    Publication Year: 2013 , Page(s): 2525 - 2535
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2834 KB) |  | HTML iconHTML  

    This paper considers decentralized state estimation subject to communication constraints. A group of agents measure the state of a process and obtain their state estimates by exchanging data with each other. Due to the communication constraint, only a few communication channels are available. The main objective of this paper is to allocate these channels among the agents so as to minimize their average estimation errors. We provide optimal allocation strategies for agents having the homogeneous and heterogeneous sensing capabilities, respectively. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Secure Communication Via an Untrusted Non-Regenerative Relay in Fading Channels

    Publication Year: 2013 , Page(s): 2536 - 2550
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4180 KB) |  | HTML iconHTML  

    We investigate a relay network where the source can potentially utilize an untrusted non-regenerative relay to augment its direct transmission of a confidential message to the destination. Since the relay is untrusted, it is desirable to protect the confidential data from it while simultaneously making use of it to increase the reliability of the transmission. We first examine the secrecy outage probability (SOP) of the network assuming a single antenna relay, and calculate the exact SOP for three different schemes: direct transmission without using the relay, conventional non-regenerative relaying, and cooperative jamming by the destination. Subsequently, we conduct an asymptotic analysis of the SOPs to determine the optimal policies in different operating regimes. We then generalize to the multi-antenna relay case and investigate the impact of the number of relay antennas on the secrecy performance. Finally, we study a scenario where the relay has only a single RF chain which necessitates an antenna selection scheme, and we show that unlike the case where all antennas are used, under certain conditions the cooperative jamming scheme with antenna selection provides a diversity advantage for the receiver. Numerical results are presented to verify the theoretical predictions of the preferred transmission policies. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Insights Into MUSIC-Like Algorithm

    Publication Year: 2013 , Page(s): 2551 - 2556
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (870 KB) |  | HTML iconHTML  

    In this work, we focus on a recent algorithm [Z. Ying and B. P. Ng, “MUSIC-like DOA Estaimation Without Estimating the Number of Sources,” IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1668-1676, 2010], which is remarked to have multiple signal classification (MUSIC)-like performance without requiring to segregate the signal and noise subspaces. The optimization problem solved by this algorithm in each look direction is analyzed to obtain insights into the working principle of the algorithm. Besides showing the similarity between this algorithm and the MUSIC algorithm, its distinction from the Capon's estimator is also highlighted. The bounds for the sole parameter embedded within the optimization problem is also discussed. Simulation results evaluate the performance of the technique in comparison with the MUSIC algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Consensus and Products of Random Stochastic Matrices: Exact Rate for Convergence in Probability

    Publication Year: 2013 , Page(s): 2557 - 2571
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4463 KB) |  | HTML iconHTML  

    We find the exact rate for convergence in probability of products of independent, identically distributed symmetric, stochastic matrices. It is well-known that if the matrices have positive diagonals almost surely and the support graph of the mean or expected value of the random matrices is connected, the products of the matrices converge almost surely to the average consensus matrix, and thus in probability. In this paper, we show that the convergence in probability is exponentially fast, and we explicitly characterize the exponential rate of this convergence. Our analysis reveals that the exponential rate of convergence in probability depends only on the statistics of the support graphs of the random matrices. Further, we show how to compute this rate for commonly used random models: gossip and link failure. With these models, the rate is found by solving a min-cut problem, and hence it is easily computable. Finally, as an illustration, we apply our results to solving power allocation among networked sensors in a consensus+innovations distributed detection problem. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Distributed Particle Filter Implementation With Intermittent/Irregular Consensus Convergence

    Publication Year: 2013 , Page(s): 2572 - 2587
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5604 KB) |  | HTML iconHTML  

    Motivated by non-linear, non-Gaussian, distributed multi-sensor/agent navigation and tracking applications, we propose a multi-rate consensus/fusion based framework for distributed implementation of the particle filter (CF/DPF). The CF/DPF framework is based on running localized particle filters to estimate the overall state vector at each observation node. Separate fusion filters are designed to consistently assimilate the local filtering distributions into the global posterior by compensating for the common past information between neighboring nodes. The CF/DPF offers two distinct advantages over its counterparts. First, the CF/DPF framework is suitable for scenarios where network connectivity is intermittent and consensus can not be reached between two consecutive observations. Second, the CF/DPF is not limited to the Gaussian approximation for the global posterior density. A third contribution of the paper is the derivation of the exact expression for computing the posterior Cramér-Rao lower bound (PCRLB) for the distributed architecture based on a recursive procedure involving the local Fisher information matrices (FIMs) of the distributed estimators. The performance of the CF/DPF algorithm closely follows the centralized particle filter approaching the PCRLB at the signal to noise ratios that we tested. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Model Estimation and Classification Via Model Structure Determination

    Publication Year: 2013 , Page(s): 2588 - 2597
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3187 KB) |  | HTML iconHTML  

    In model estimation, we often face problems with unknown parameters in the candidate models. This paper proposes the model structure determination (MSD) for model estimation with unknown parameters. We start with the problem of model order selection and decompose the probability density function (PDF) into the information provided by the data about the model parameters and that of the model structure. The factor that depends on the model parameters is approximated using a minimax procedure, and the MSD depends on the model structure only. It is shown that the MSD is equivalent to the exponentially embedded family (EEF) for model order selection under some conditions. Finally, we apply the MSD to a classification problem where we have partial knowledge about the parameters, and simulation results show that it outperforms the pseudo-maximum-likelihood (pseudo-ML) rule. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Limited Feedback Design for Interference Alignment on MIMO Interference Networks With Heterogeneous Path Loss and Spatial Correlations

    Publication Year: 2013 , Page(s): 2598 - 2607
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2640 KB) |  | HTML iconHTML  

    Interference alignment is degree of freedom optimal on -user MIMO interference channels and many previous works have studied the transceiver designs. However, these works predominantly focus on networks with perfect channel state information at the transmitters and symmetrical interference topology. In this paper, we consider a limited feedback system with heterogeneous path loss and spatial correlations and investigate how the dynamics of the interference topology can be exploited to improve the feedback efficiency. We propose a novel spatial codebook design and perform dynamic quantization via bit allocations to adapt to the asymmetry of the interference topology. We bound the system throughput under the proposed dynamic scheme in terms of the transmit SNR, feedback bits, and the interference topology parameters. It is shown that when the number of feedback bits scales with SNR as Cs·log SNR +O(1), the sum degrees of freedom of the network are preserved. Moreover, the value of scaling coefficient Cs can be significantly reduced in networks with asymmetric interference topology. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Novel Dynamic Programming Algorithm for Track-Before-Detect in Radar Systems

    Publication Year: 2013 , Page(s): 2608 - 2619
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2327 KB) |  | HTML iconHTML  

    In this paper we present a novel procedure for multi-frame detection in radar systems. The proposed architecture consists of a pre-processing stage, which extracts a set of candidate alarms (or plots) from the raw data measurements (e.g., this can be the Detector and Plot-Extractor of common radar systems), and a track-before-detect (TBD) processor, which jointly elaborates observations from multiple scans (or frames) and confirms reliable plots. A computationally efficient dynamic programming algorithm for the TBD processor is derived, which does not require a discretization of the state space and operates directly on the input plot-lists. Finally, a simple algorithm to solve possible data association problems arising at the track-formation step is given, and a thorough complexity and performance analysis is provided, showing that large detection gains with respect to the standard radar processing are achievable with negligible complexity increase. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Full Rank Solutions for the MIMO Gaussian Wiretap Channel With an Average Power Constraint

    Publication Year: 2013 , Page(s): 2620 - 2631
    Cited by:  Papers (6)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3186 KB) |  | HTML iconHTML  

    This paper considers a multiple-input multiple-output (MIMO) Gaussian wiretap channel with a transmitter, a legitimate receiver and an eavesdropper, each equipped with multiple antennas. We first study the rank of the optimal input covariance matrix that achieves the secrecy capacity of the MIMO Gaussian wiretap channel under an average power constraint. The rank and other properties of the optimal solution are derived based on certain relationships between the channel matrices for the legitimate receiver and eavesdropper. Next, by obtaining necessary and sufficient conditions on the MIMO wiretap channel parameters, we determine the conditions under which the optimal input covariance matrix is full-rank or rank-deficient. For the case that the optimal input covariance is full-rank, we fully characterize the solution. When the optimal input covariance is rank-deficient, we show that the given MIMO wiretap channel can be modeled by an equivalent wiretap channel whose optimal input covariance is full rank and achieves the same secrecy capacity as the original system. Numerical results are presented to illustrate the proposed theoretical findings. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Recursive Form of Error Bounds for RFS State and Observation With P_d < 1

    Publication Year: 2013 , Page(s): 2632 - 2646
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (4671 KB) |  | HTML iconHTML  

    This paper presents recursive performance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (Pd <; 1), we must determine the existence or nonexistence of the state as well as its value. Following the possible detection/miss sequences within the framework of a random vector, possible observation sets sequences are first defined. Based on these sequences, the performance bounds can be represented by a multivariate function of some time-based auxiliary elements. Recursive relations for all the auxiliary elements are derived rigorously. For the multitarget case, this recursive estimated bound can be applied without data association. Applications are analyzed through simulations to verify our theoretical results and show that our bounds are tighter than all other bounds for the case where detection probability Pd <; 1. View full abstract»

    Open Access
  • An Energy-Efficient Framework for the Analysis of MIMO Slow Fading Channels

    Publication Year: 2013 , Page(s): 2647 - 2659
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2802 KB) |  | HTML iconHTML  

    In this paper, a new energy-efficiency performance metric is proposed for multiple-input multiple-output (MIMO) point-to-point systems. In contrast with related works on energy-efficiency, this metric translates the effects of using finite blocks for transmitting, using channel estimates at the transmitter and receiver, and considering the total power consumed by the transmitter instead of the radiated power only. The main objective pursued is to choose the best precoding matrix used at the transmitter in the following two scenarios : 1) the one where imperfect channel state information (CSI) is available at the transmitter and receiver and 2) the one where no CSI is available at the transmitter. In both scenarios, the problem of optimally tuning the total used power is shown to be nontrivial. In scenario 2), the optimal fraction of training time can be characterized by a simple equation. These results and others provided in the paper, along with the provided numerical analysis, show that the present work can therefore be used as a good basis for studying power control and resource allocation in energy-efficient multiuser networks. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Joint Multicast Beamforming and Antenna Selection

    Publication Year: 2013 , Page(s): 2660 - 2674
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3153 KB) |  | HTML iconHTML  

    Multicast beamforming exploits subscriber channel state information at the base station to steer the transmission power towards the subscribers, while minimizing interference to other users and systems. Such functionality has been provisioned in the long-term evolution (LTE) enhanced multimedia broadcast multicast service (EMBMS). As antennas become smaller and cheaper relative to up-conversion chains, transmit antenna selection at the base station becomes increasingly appealing in this context. This paper addresses the problem of joint multicast beamforming and antenna selection for multiple co-channel multicast groups. Whereas this problem (and even plain multicast beamforming) is NP-hard, it is shown that the mixed l1,∞-norm squared is a prudent group-sparsity inducing convex regularization, in that it naturally yields a suitable semidefinite relaxation, which is further shown to be the Lagrange bi-dual of the original NP-hard problem. Careful simulations indicate that the proposed algorithm significantly reduces the number of antennas required to meet prescribed service levels, at relatively small excess transmission power. Furthermore, its performance is close to that attained by exhaustive search, at far lower complexity. Extensions to max-min-fair, robust, and capacity-achieving designs are also considered. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Phase Noise in MIMO Systems: Bayesian Cramér–Rao Bounds and Soft-Input Estimation

    Publication Year: 2013 , Page(s): 2675 - 2692
    Cited by:  Papers (2)
    Multimedia
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (5977 KB)  

    This paper addresses the problem of estimating time varying phase noise caused by imperfect oscillators in multiple-input multiple-output (MIMO) systems. The estimation problem is parameterized in detail and based on an equivalent signal model its dimensionality is reduced to minimize the overhead associated with phase noise estimation. New exact and closed-form expressions for the Bayesian Cramér-Rao lower bounds (BCRLBs) and soft-input maximum a posteriori (MAP) estimators for online, i.e., filtering, and offline, i.e., smoothing, estimation of phase noise over the length of a frame are derived. Simulations demonstrate that the proposed MAP estimators' mean-square error (MSE) performances are very close to the derived BCRLBs at moderate-to-high signal-to-noise ratios. To reduce the overhead and complexity associated with tracking the phase noise processes over the length of a frame, a novel soft-input extended Kalman filter (EKF) and extended Kalman smoother (EKS) that use soft statistics of the transmitted symbols given the current observations are proposed. Numerical results indicate that by employing the proposed phase tracking approach, the bit-error rate performance of a MIMO system affected by phase noise can be significantly improved. In addition, simulation results indicate that the proposed phase noise estimation scheme allows for application of higher order modulations and larger numbers of antennas in MIMO systems that employ imperfect oscillators. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Frugal Sensing: Wideband Power Spectrum Sensing From Few Bits

    Publication Year: 2013 , Page(s): 2693 - 2703
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2517 KB) |  | HTML iconHTML  

    Wideband spectrum sensing is a key requirement for cognitive radio access. It now appears increasingly likely that spectrum sensing will be performed using networks of sensors, or crowd-sourced to handheld mobile devices. Here, a network sensing scenario is considered, where scattered low-end sensors filter and measure the average signal power across a band of interest, and each sensor communicates a single bit (or coarsely quantized level) to a fusion center, depending on whether its measurement is above a certain threshold. The focus is on the underdetermined case, where relatively few bits are available at the fusion center. Exploiting non-negativity and the linear relationship between the power spectrum and the autocorrelation, it is shown that adequate power spectrum sensing is possible from few bits, even for dense spectra. The formulation can be viewed as generalizing classical nonparametric power spectrum estimation to the case where the data is in the form of inequalities, rather than equalities. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IEEE Transactions on Signal Processing covers novel theory, algorithms, performance analyses and applications of techniques for the processing, understanding, learning, retrieval, mining, and extraction of information from signals

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Sergios Theodoridis
University of Athens