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Signal Processing, IEEE Transactions on

Issue 2 • Date Feb. 2011

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Displaying Results 1 - 25 of 42
  • Table of contents

    Publication Year: 2011 , Page(s): C1 - C4
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    Freely Available from IEEE
  • IEEE Transactions on Signal Processing publication information

    Publication Year: 2011 , Page(s): C2
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    Freely Available from IEEE
  • Decentralized Particle Filter With Arbitrary State Decomposition

    Publication Year: 2011 , Page(s): 465 - 478
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (760 KB) |  | HTML iconHTML  

    In this paper, a new particle filter (PF) which we refer to as the decentralized PF (DPF) is proposed. By first decomposing the state into two parts, the DPF splits the filtering problem into two nested subproblems and then handles the two nested subproblems using PFs. The DPF has the advantage over the regular PF that the DPF can increase the level of parallelism of the PF. In particular, part of the resampling in the DPF bears a parallel structure and can thus be implemented in parallel. The parallel structure of the DPF is created by decomposing the state space, differing from the parallel structure of the distributed PFs which is created by dividing the sample space. This difference results in a couple of unique features of the DPF in contrast with the existing distributed PFs. Simulation results of two examples indicate that the DPF has a potential to achieve in a shorter execution time the same level of performance as the regular PF. View full abstract»

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  • Estimating the Order of an Autoregressive Model Using Normalized Maximum Likelihood

    Publication Year: 2011 , Page(s): 479 - 487
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (363 KB) |  | HTML iconHTML  

    This paper examines the estimation of the order of an autoregressive model using the minimum description length principle. A closed form for an approximation of the parametric complexity of the autoregressive model class is derived by exploiting a relationship between coefficients and partial autocorrelations. The parametric complexity over the complete parameter space is found to diverge. A model selection criterion is subsequently derived by bounding the parameter space, and simulations suggest that it compares well against standard autoregressive order selection techniques in terms of correct order identification and prediction error. View full abstract»

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  • Noise Benefits in Quantizer-Array Correlation Detection and Watermark Decoding

    Publication Year: 2011 , Page(s): 488 - 505
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1754 KB) |  | HTML iconHTML  

    Quantizer noise can improve statistical signal detection in array-based nonlinear correlators in Neyman-Pearson and maximum-likelihood (ML) detection. This holds even for infinite-variance symmetric alpha-stable channel noise and for generalized-Gaussian channel noise. Noise-enhanced correlation detection leads to noise-enhanced watermark extraction based on such nonlinear detection at the pixel or bit level. This yields a noise-based algorithm for digital watermark decoding using two new noise-benefit theorems. The first theorem gives a necessary and sufficient condition for quantizer noise to increase the detection probability of a constant signal for a fixed false-alarm probability if the channel noise is symmetric and if the sample size is large. The second theorem shows that the array must contain more than one quantizer for such a stochastic-resonance noise benefit if the symmetric channel noise is unimodal. It also shows that the noise-benefit rate improves in the small-quantizer noise limit as the number of array quantizers increases. The second theorem further shows that symmetric uniform quantizer noise gives the optimal rate for an initial noise benefit among all finite-variance symmetric scale-family noise. Two corollaries give similar results for stochastic-resonance noise benefits in ML detection of a signal sequence with known shape but unknown amplitude. View full abstract»

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  • On the Optimal Stacking of Information-Plus-Noise Matrices

    Publication Year: 2011 , Page(s): 506 - 514
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (312 KB) |  | HTML iconHTML  

    Observations of the form D + X, where D is a matrix representing information, and X is a random matrix representing noise, can be grouped into a compound observation matrix, on the same information + noise form. There are many ways the observations can be stacked into such a matrix, for instance vertically, horizontally, or quadratically. An unbiased estimator for the spectrum of D can be formulated for each stacking scenario in the case of Gaussian noise. We compare these spectrum estimators for the different stacking scenarios, and show that all kinds of stacking actually decrease the variance of the corresponding spectrum estimators when compared to just taking an average of the observations, and find which stacking is optimal in this sense. When the number of observations grow, however, it is shown that the difference between the estimators is marginal, with only the cases of vertical and horizontal stackings having a higher variance asymptotically. View full abstract»

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  • Circularity of the STFT and Spectral Kurtosis for Time-Frequency Segmentation in Gaussian Environment

    Publication Year: 2011 , Page(s): 515 - 524
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1094 KB) |  | HTML iconHTML  

    This paper investigates the circularity of short time Fourier transform (STFT) coefficients noise only, and proposes a modified STFT such that all coefficients coming from white Gaussian noise are circular. In order to use the spectral kurtosis (SK) as a Gaussianity test to check if signal points are present in a set of STFT points, we consider the SK of complex circular random variables, and its link with the kurtosis of the real and imaginary parts. We show that the variance of the SK is smaller than the variance of the kurtosis estimated from both real and imaginary parts. The effect of the noncircularity of Gaussian variables upon the spectral kurtosis of STFT coefficients is studied, as well as the effect of signal presence. Finally, a time-frequency segmentation algorithm based on successive iterations of noise variance estimation and time-frequency coefficients detection is proposed. The iterations are stopped when the spectral kurtosis on nondetected points reaches zero. Examples of segmented time-frequency space are presented on a dolphin whistle and on a simulated signal in nonwhite and nonstationary Gaussian noise. View full abstract»

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  • Sampling of Spectrally Correlated Processes

    Publication Year: 2011 , Page(s): 525 - 539
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2257 KB) |  | HTML iconHTML  

    The problem of sampling continuous-time spectrally correlated (SC) processes is addressed. SC processes have Loève bifrequency spectrum with spectral masses concentrated on a countable set of support curves in the bifrequency plane. This class of nonstationary processes extends that of the almost-cyclostationary processes and occurs in wideband mobile communications and spectral analysis with nonuniform spectral resolution. The class of the discrete-time SC processes is introduced and characterized. It is shown that such processes can be obtained by uniformly sampling the continuous-time SC processes. Sampling theorems are presented and sufficient conditions to avoid aliasing are provided. Applications are illustrated with reference to the problem of cross spectral analysis with nonuniform frequency resolution and the propagation of communications signals through MIMO multipath Doppler channels. View full abstract»

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  • Blind Identification of Underdetermined Mixtures Based on the Characteristic Function: The Complex Case

    Publication Year: 2011 , Page(s): 540 - 553
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1110 KB) |  | HTML iconHTML  

    Blind identification of underdetermined mixtures can be addressed efficiently by using the second ChAracteristic Function (CAF) of the observations. Our contribution is twofold. First, we propose the use of a Levenberg-Marquardt algorithm, herein called LEMACAF, as an alternative to an Alternating Least Squares algorithm known as ALESCAF, which has been used recently in the case of real mixtures of real sources. Second, we extend the CAF approach to the case of complex sources for which the previous algorithms are not suitable. We show that the complex case involves an appropriate tensor stowage, which is linked to a particular tensor decomposition. An extension of the LEMACAF algorithm, called then proposed to blindly estimate the mixing matrix by exploiting this tensor decomposition. In our simulation results, we first provide performance comparisons between third- and fourth-order versions of ALESCAF and LEMACAF in various situations involving BPSK sources. Then, a performance study of is carried out considering 4-QAM sources. These results show that the proposed algorithm provides satisfying estimations especially in the case of a large underdeterminacy level. View full abstract»

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  • Segmented Compressed Sampling for Analog-to-Information Conversion: Method and Performance Analysis

    Publication Year: 2011 , Page(s): 554 - 572
    Cited by:  Papers (10)
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    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (697 KB)  

    A new segmented compressed sampling (CS) method for analog-to-information conversion (AIC) is proposed. An analog signal measured by a number of parallel branches of mixers and integrators (BMIs), each characterized by a specific random sampling waveform, is first segmented in time into segments. Then the subsamples collected on different segments and different BMIs are reused so that a larger number of samples (at most ) than the number of BMIs is collected. This technique is shown to be equivalent to extending the measurement matrix, which consists of the BMI sampling waveforms, by adding new rows without actually increasing the number of BMIs. We prove that the extended measurement matrix satisfies the restricted isometry property with overwhelming probability if the original measurement matrix of BMI sampling waveforms satisfies it. We also prove that the signal recovery performance can be improved if our segmented CS-based AIC is used for sampling instead of the conventional AIC with the same number of BMIs. Therefore, the reconstruction quality can be improved by slightly increasing (by times) the sampling rate per each BMI. Simulation results verify the effectiveness of the proposed segmented CS method and the validity of our theoretical results. Particularly, our simulation results show significant signal recovery performance improvement when the segmented CS-based AIC is used instead of the conventional AIC with the same number of BMIs. View full abstract»

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  • Sparse Sensing With Co-Prime Samplers and Arrays

    Publication Year: 2011 , Page(s): 573 - 586
    Cited by:  Papers (48)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (975 KB) |  | HTML iconHTML  

    This paper considers the sampling of temporal or spatial wide sense stationary (WSS) signals using a co-prime pair of sparse samplers. Several properties and applications of co-prime samplers are developed. First, for uniform spatial sampling with M and N sensors where M and N are co-prime with appropriate interelement spacings, the difference co-array has O(MN) freedoms which can be exploited in beamforming and in direction of arrival estimation. An M -point DFT filter bank and an N-point DFT filter bank can be used at the outputs of the two sensor arrays and their outputs combined in such a way that there are effectively MN bands (i.e., MN narrow beams with beamwidths proportional to 1/MN), a result following from co-primality. The ideas are applicable to both active and passive sensing, though the details and tradeoffs are different. Time domain sparse co-prime samplers also generate a time domain co-array with O(MN) freedoms, which can be used to estimate the autocorrelation at much finer lags than the sample spacings. This allows estimation of power spectrum of an arbitrary signal with a frequency resolution proportional to 2π/(MNT) even though the pairs of sampled sequences xc(NTn) and xc(MTn) in the time domain can be arbitrarily sparse - in fact from the sparse set of samples xc(NTn) and xc(MTn) one can estimate O(MN) frequencies in the range |ω| <; π/T. It will be shown that the co-array based method for estimating sinusoids in noise offers many advantages over methods based on the use of Chinese remainder theorem and its extensions. Examples are presented throughout to illustrate the various concepts. View full abstract»

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  • On the Estimation of Nonrandom Signal Coefficients From Jittered Samples

    Publication Year: 2011 , Page(s): 587 - 597
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (598 KB) |  | HTML iconHTML  

    This paper examines the problem of estimating the parameters of a bandlimited signal from samples corrupted by random jitter (timing noise) and additive, independent identically distributed (i.i.d.) Gaussian noise, where the signal lies in the span of a finite basis. For the presented classical estimation problem, the Cramér-Rao lower bound (CRB) is computed, and an Expectation-Maximization (EM) algorithm approximating the maximum likelihood (ML) estimator is developed. Simulations are performed to study the convergence properties of the EM algorithm and compare the performance both against the CRB and a basic linear estimator. These simulations demonstrate that by postprocessing the jittered samples with the proposed EM algorithm, greater jitter can be tolerated, potentially reducing on-chip ADC power consumption substantially. View full abstract»

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  • Transform Order Division Multiplexing

    Publication Year: 2011 , Page(s): 598 - 609
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (797 KB) |  | HTML iconHTML  

    Multiplexing in the fractional Fourier domain (FRFD) only transmits the signals in a single direction in the time-frequency plane and the transform order of the fractional Fourier transform (FRFT) is not used sufficiently. In this paper, we apply the perfect reconstruction transmultiplexer in the FRFD to develop the transform order division multiplexing (TODM), which transmits the original information in the multidirections in the time-frequency plane simultaneously. The proposed TODM systems can be divided into two types. One needs the multitransmission of the original information while the other needs the modifications of the original information by the interpolation and the inverse discrete FRFT. The performance analysis of the TODM systems is also presented, including the system capacity, the reconstruction accuracy, the security, and the limitation. Compared with the multiplexing in the FRFD, the TODM is more flexible and the security of the system is highly strengthened. Finally, simulation results confirm the obtained analytical results. View full abstract»

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  • Radar HRRP Statistical Recognition With Local Factor Analysis by Automatic Bayesian Ying-Yang Harmony Learning

    Publication Year: 2011 , Page(s): 610 - 617
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (865 KB) |  | HTML iconHTML  

    Radar high-resolution range profiles (HRRPs) are typical high-dimensional, non-Gaussian and interdimension dependently distributed data, the statistical modelling of which is a challenging task for HRRP based target recognition. Assuming the HRRP data follow interdimension dependent Gaussian distribution, factor analysis (FA) was recently applied to describe radar HRRPs and a two-phase procedure was used for model selection, showing promising recognition results. Besides the interdimensional dependence, this paper further models the non-Gaussianity of the radar HRRP data by local factor analysis (LFA). Moreover, since the two-phase procedure suffers from extensive computation and inaccurate evaluation on high-dimensional finite HRRPs, we adopt an automatic Bayesian Ying-Yang (BYY) harmony learning, which determines the component number and the hidden dimensionalities of LFA automatically during parameter learning. Experimental results show incremental improvements on recognition accuracy by three implementations, progressively from a two-phase FA, to a two-phase LFA, and then to an automatically learned LFA by BYY harmony learning. View full abstract»

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  • Wideband MIMO Systems: Signal Design for Transmit Beampattern Synthesis

    Publication Year: 2011 , Page(s): 618 - 628
    Cited by:  Papers (10)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1837 KB) |  | HTML iconHTML  

    The usage of multi-input multi-output (MIMO) systems such as a MIMO radar allows the array elements to transmit different waveforms freely. This waveform diversity can lead to flexible transmit beampattern synthesis, which is useful in many applications such as radar/sonar and biomedical imaging. In the past literature most attention was paid to receive beampattern design due to the stringent constraints on waveforms in the transmit beampattern case. Recently progress has been made on MIMO transmit beampattern synthesis but mainly only for narrowband signals. In this paper we propose a new approach that can be used to efficiently synthesize MIMO waveforms in order to match a given wideband transmit beampattern, i.e., to match a transmit energy distribution in both space and frequency. The synthesized waveforms satisfy the unit-modulus or low peak-to-average power ratio (PAR) constraints that are highly desirable in practice. Several examples are provided to investigate the performance of the proposed approach. View full abstract»

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  • SPICE: A Sparse Covariance-Based Estimation Method for Array Processing

    Publication Year: 2011 , Page(s): 629 - 638
    Cited by:  Papers (38)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (470 KB) |  | HTML iconHTML  

    This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing. The proposed approach is obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many-snapshot cases but can be used even in single-snapshot situations. SPICE has several unique features not shared by other sparse estimation methods: it has a simple and sound statistical foundation, it takes account of the noise in the data in a natural manner, it does not require the user to make any difficult selection of hyperparameters, and yet it has global convergence properties. View full abstract»

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  • Multiobjective Optimization of OFDM Radar Waveform for Target Detection

    Publication Year: 2011 , Page(s): 639 - 652
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1446 KB) |  | HTML iconHTML  

    We propose a multiobjective optimization (MOO) technique to design an orthogonal-frequency-division multiplexing (OFDM) radar signal for detecting a moving target in the presence of multipath reflections. We employ an OFDM signal to increase the frequency diversity of the system, as different scattering centers of a target resonate variably at different frequencies. Moreover, the multipath propagation increases the spatial diversity by providing extra “looks” at the target. First, we develop a parametric OFDM radar model by reformulating the target-detection problem as the task of sparse-signal spectrum estimation. At a particular range cell, we exploit the sparsity of multiple paths and the knowledge of the environment to estimate the paths along which the target responses are received. Then, to estimate the sparse vector, we employ a collection of multiple small Dantzig selectors (DS) that utilizes more prior structures of the sparse vector. We use the ℓ1-constrained minimal singular value (ℓ1-CMSV) of the measurement matrix to analytically evaluate the reconstruction performance and demonstrate that our decomposed DS performs better than the standard DS. In addition, we propose a constrained MOO-based algorithm to optimally design the spectral parameters of the OFDM waveform for the next coherent processing interval by simultaneously optimizing two objective functions: minimizing the upper bound on the estimation error to improve the efficiency of sparse-recovery and maximizing the squared Mahalanobis-distance to increase the performance of the underlying detection problem. We provide a few numerical examples to illustrate the performance characteristics of the sparse recovery and demonstrate the achieved performance improvement due to adaptive OFDM waveform design. View full abstract»

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  • Low-Delay Prediction- and Transform-Based Wyner–Ziv Coding

    Publication Year: 2011 , Page(s): 653 - 666
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1379 KB) |  | HTML iconHTML  

    This paper studies low-delay Wyner-Ziv coding, i.e., lossy source coding with side information at the decoder, with emphasis on the extreme of zero delay. To achieve zero delay, a scalar quantizer is followed by scalar coding of quantization indices. In the fixed-length coding scenario, under high-resolution assumptions and appropriately defined decodability constraints, the optimal quantization level density is conjectured to be periodic. This conjecture, which is provable when the correlation is high, allows for a precise analysis of the rate-distortion tradeoff. The performance of variable-length coding with periodic quantization is also characterized. The results are then incorporated in predictive Wyner-Ziv coding for Gaussian sources with memory, and optimal prediction filters are numerically designed so as to strike a balance between maximally exploiting both temporal and spatial correlation and limiting the propagation of distortion due to occasional decoding errors. Finally, the zero-delay schemes are also employed in transform coding with small block lengths, where the Gaussian source and side information are transformed separately with the premise that corresponding transform coefficient pairs exhibit good spatial correlation and minimal temporal correlation. For the specific source-side information pairs studied, it is shown that transform coding, even with a small block-length, outperforms predictive coding. Performances of both predictive and transform coding are also compared with the asymptotic rate-distortion bounds. View full abstract»

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  • Bayesian and Hybrid Cramér–Rao Bounds for the Carrier Recovery Under Dynamic Phase Uncertain Channels

    Publication Year: 2011 , Page(s): 667 - 680
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    In this paper, we study Bayesian and hybrid Cramér-Rao bounds (BCRB and HCRB) for the code-aided (CA), the data-aided (DA), and the non-data-aided (NDA) dynamical phase estimation of QAM modulated signals. We address the bounds derivation for both the offline scenario, for which the whole observation frame is used, and the online which only takes into account the current and the previous observations. For the CA scenario we show that the computation of the Bayesian information matrix (BIM) and of the hybrid information matrix (HIM) is NP hard. We then resort to the belief-propagation (BP) algorithm or to the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm to obtain some approximate values. Moreover, in order to avoid the calculus of the inverse of the BIM and of the HIM, we present some closed form expressions for the various CRBs, which greatly reduces the computation complexity. Finally, some simulations allow us to compare the possible improvements enabled by the offline and the CA scenarios. View full abstract»

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  • Blind Adaptive Constrained Constant-Modulus Reduced-Rank Interference Suppression Algorithms Based on Interpolation and Switched Decimation

    Publication Year: 2011 , Page(s): 681 - 695
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1540 KB) |  | HTML iconHTML  

    This work proposes a blind adaptive reduced-rank scheme and constrained constant-modulus (CCM) adaptive algorithms for interference suppression in wireless communications systems. The proposed scheme and algorithms are based on a two-stage processing framework that consists of a transformation matrix that performs dimensionality reduction followed by a reduced-rank estimator. The complex structure of the transformation matrix of existing methods motivates the development of a blind adaptive reduced-rank constrained (BARC) scheme along with a low-complexity reduced-rank decomposition. The proposed BARC scheme and a reduced-rank decomposition based on the concept of joint interpolation, switched decimation and reduced-rank estimation subject to a set of constraints are then detailed. The proposed set of constraints ensures that the multipath components of the channel are combined prior to dimensionality reduction. We develop low-complexity joint interpolation and decimation techniques, stochastic gradient, and recursive least squares reduced-rank estimation algorithms. A model-order selection algorithm for adjusting the length of the estimators is devised along with techniques for determining the required number of switching branches to attain a predefined performance. An analysis of the convergence properties and issues of the proposed optimization and algorithms is carried out, and the key features of the optimization problem are discussed. We consider the application of the proposed algorithms to interference suppression in DS-CDMA systems. The results show that the proposed algorithms outperform the best known reduced-rank schemes, while requiring lower complexity. View full abstract»

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  • Physical Layer Network Coding and Precoding for the Two-Way Relay Channel in Cellular Systems

    Publication Year: 2011 , Page(s): 696 - 712
    Cited by:  Papers (25)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (671 KB) |  | HTML iconHTML  

    In this paper, we study the application of physical layer network coding to the joint design of uplink and downlink transmissions, where the base station and the relay have multiple antennas, and all mobile stations only have a single antenna. A new network coding transmission protocol is proposed, where uplink and downlink transmissions can be accomplished within two time slots. Since each single antenna user has poor receive capability, precoding at the base station and relay has been carefully designed to ensure that co-channel interference can be removed completely. Explicit analytic results have been developed to demonstrate that the multiplexing gain achieved by the proposed transmission protocol is , much better than existing time sharing schemes. To further increase the achievable diversity gain, two variations of the proposed transmission protocols have also been proposed when there are multiple relays and the number of the antennas at the base station and relay is increased. Monte-Carlo simulation results have also been provided to demonstrate the performance of the proposed network coded transmission protocol. View full abstract»

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  • Block Diagonal GMD for Zero-Padded MIMO Frequency Selective Channels

    Publication Year: 2011 , Page(s): 713 - 727
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1111 KB) |  | HTML iconHTML  

    In the class of systems with linear precoder and decision feedback equalizers (DFE) for zero-padded (ZP) multiple-input multiple-output (MIMO) frequency selective channels, existing optimal transceiver designs present two drawbacks. First, the optimal systems require a large number of feedback bits from the receiver to encode the full precoding matrix. Second, the full precoding matrix leads to complex computations. These disadvantages become more severe as the bandwidth (BW) efficiency increases. In this paper, we propose using block diagonal geometric mean decomposition (BD-GMD) to design the transceiver. Two new BD-GMD transceivers are proposed: the ZF-BD-GMD system, where the receiver is a zero-forcing DFE (ZF-DFE), and the MMSE-BD-GMD system, where the receiver is a minimum- mean-square-error DFE (MMSE-DFE). The BD-GMD systems introduced here have the following four properties: a) They use the block diagonal unitary precoding technique to reduce the required number of encoding bits and simplify the computation. b) For any block size, the BD-GMD systems are optimal within the family of systems using block diagonal unitary precoders and DFEs. As block size gets larger, the BD-GMD systems produce uncoded bit error rate (BER) performance similar to the optimal systems using unitary precoders and DFEs. c) For the two ZF transceivers (ZF-Optimal and ZF-BD-GMD) and the two MMSE transceivers (MMSE-Optimal and MMSE-BD-GMD), the average BER degrades as the BW efficiency increases. d) In the case of single-input single-output (SISO) channels, the BD-GMD systems have the same performance as those of the lazy precoder transceivers. These properties make the proposed BD-GMD systems more favorable designs in practical implementation than the optimal systems. View full abstract»

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  • Distributed Multicell Beamforming With Limited Intercell Coordination

    Publication Year: 2011 , Page(s): 728 - 738
    Cited by:  Papers (35)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1094 KB) |  | HTML iconHTML  

    This paper studies distributed optimization schemes for multicell joint beamforming and power allocation in time-division-duplex (TDD) multicell downlink systems where only limited-capacity intercell information exchange is permitted. With an aim to maximize the worst-user signal-to-interference-and-noise ratio (SINR), we devise a hierarchical iterative algorithm to optimize downlink beamforming and intercell power allocation jointly in a distributed manner. The proposed scheme is proved to converge to the global optimum. For fast convergence and to reduce the burden of intercell parameter exchange, we further propose to exploit previous iterations adaptively. Results illustrate that the proposed scheme can achieve near-optimal performance even with a few iterations, hence providing a good tradeoff between performance and backhaul consumption. The performance under quantized parameter exchange is also examined. View full abstract»

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  • Investigation on the FFT-Based Antenna Selection for Compact Uniform Circular Arrays in Correlated MIMO Channels

    Publication Year: 2011 , Page(s): 739 - 746
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (578 KB) |  | HTML iconHTML  

    Using the beamspace preprocessing in RF chains, the fast Fourier transform (FFT)-based antenna selection scheme can reduce the performance degradation of traditional antenna selection schemes in correlated multiple-input multiple-output (MIMO) channels. Based on this technique, an antenna selection method in beamspace is developed for MIMO systems using compact uniform circular arrays (UCAs) at the receiver. To take advantages of the FFT-based antenna selection scheme for application in practical scenarios, a parametric physical model that considers the geometrical properties of the scattering environment is introduced to include realistic fading conditions into the channel matrix. Furthermore, due to the limited spatial phase modes of UCAs, the channel matrix resulting from the beamspace preprocessing only possesses a limited and small number of nonzero rows. This substantially reduces the computational load in the following beam selection procedure. More importantly, the optimal beam selection can be realized even without channel state information (CSI) at the receiver. This characteristic is especially useful for compact UCAs with a large number of elements. Besides, it is also found that the severe mutual coupling effect resulting from compact UCAs does not affect these favorable characteristics of the FFT-based preprocessing technique. Numerical examples considering strong mutual coupling in compact UCAs are provided to verify and validate the proposed method. View full abstract»

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  • Cooperative Feedback for Multiantenna Cognitive Radio Networks

    Publication Year: 2011 , Page(s): 747 - 758
    Cited by:  Papers (21)
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    Cognitive beamforming (CB) is a multiantenna technique for efficient spectrum sharing between primary users (PUs) and secondary users (SUs) in a cognitive radio network. Specifically, a multiantenna SU transmitter applies CB to suppress the interference to the PU receivers as well as enhance the corresponding SU-link performance. In this paper, for a multiple-input-single-output (MISO) SU channel coexisting with a single-input-single-output (SISO) PU channel, we propose a new and practical paradigm for designing CB based on the finite-rate cooperative feedback from the PU receiver to the SU transmitter. Specifically, the PU receiver communicates to the SU transmitter the quantized SU-to-PU channel direction information (CDI) for computing the SU transmit beamformer, and the interference power control (IPC) signal that regulates the SU transmission power according to the tolerable interference margin at the PU receiver. Two CB algorithms based on the PU's cooperative feedback are proposed: one restricts the SU transmit beamformer to be orthogonal to the quantized SU-to-PU channel direction and the other relaxes such a constraint. In addition, cooperative feedforward of the SU CDI from the SU transmitter to the PU receiver is exploited to allow more efficient cooperative feedback from the PU receiver. The outage probabilities of the SU link for different cooperative feedback/feedforward algorithms are analyzed, from which the optimal bit-allocation tradeoff between the CDI and IPC feedback is characterized. View full abstract»

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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

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Sergios Theodoridis
University of Athens