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Reconfigurable intelligent surfaces (RIS) can actively perform beamforming and have become a crucial enabler for wireless systems in the future. The direction-of-arrival (DOA) estimates of RIS received signals can help design the reflection control matrix and improve communication quality. In this paper, we design a RIS-assisted system and propose a robust Lawson norm-based multiple-signal-classification (LN-MUSIC) DOA estimation algorithm for impulsive noise, which is divided into two parts. The first one, the non-convex Lawson norm is used as the error criterion along with a regularization constraint to formulate the optimization problem. Then, a Bregman distance based alternating direction method of multipliers is used to solve the problem and recover the desired signal. The second part is to use the multiple signal classification (MUSIC) to find out the DOAs of targets based on their sparsity in the spatial domain. In addition, we also propose a RIS control matrix optimization strategy that requires no channel state information, which effectively enhances the desired signals and improves the performance of the LN-MUSIC algorithm. A Cramer-Rao-lower-bound (CRLB) of the proposed DOA estimation algorithm is presented and verifies its feasibility. Simulated results show that the proposed robust DOA estimation algorithm based on the Lawson norm can effectively suppress the impact of large outliers caused by impulsive noise on the estimation results, outperforming existing methods., Comment: 10 figures, 28 pages

User authentication in future wireless communication networks is expected to become more complicated due to their large scale and heterogeneity. Furthermore, the computational complexity of classical cryptographic approaches based on public key distribution can be a limiting factor for using in simple, low-end Internet of things (IoT) devices. This paper proposes physical layer authentication (PLA) expected to complement existing traditional approaches, e.g., in multi-factor authentication protocols. The precision and consistency of PLA is impacted because of random variations of wireless channel realizations between different time slots, which can impair authentication performance. In order to address this, a method based on error-correcting codes in the form of reconciliation is considered in this work. In particular, we adopt distributed source coding (Slepian-Wolf) reconciliation using polar codes to reconcile channel measurements spread in time. Hypothesis testing is then applied to the reconciled vectors to accept or reject the device as authenticated. Simulation results show that the proposed PLA using reconciliation outperforms prior schemes even in low signal-to-noise ratio scenarios.

In this paper, channel estimation problem for extremely large-scale multi-input multi-output (XL-MIMO) systems is investigated with the considerations of the spherical wavefront effect and the spatially non-stationary (SnS) property. Due to the diversities of SnS characteristics among different propagation paths, the concurrent channel estimation of multiple paths becomes intractable. To address this challenge, we propose a two-phase channel estimation scheme. In the first phase, the angles of departure (AoDs) on the user side are estimated, and a carefully designed pilot transmission scheme enables the decomposition of the received signal from different paths. In the second phase, the subchannel estimation corresponding to different paths is formulated as a three-layer Bayesian inference problem. Specifically, the first layer captures block sparsity in the angular domain, the second layer promotes SnS property in the antenna domain, and the third layer decouples the subchannels from the observed signals. To efficiently facilitate Bayesian inference, we propose a novel three-layer generalized approximate message passing (TL-GAMP) algorithm based on structured variational massage passing and belief propagation rules. Simulation results validate the convergence and effectiveness of the proposed algorithm, showcasing its robustness to different channel scenarios., Comment: A revised manuscript has been submitted to the IEEE journal for possible publication

In this work, we investigate the joint visibility region (VR) detection and channel estimation (CE) problem for extremely large-scale multiple-input-multiple-output (XL-MIMO) systems considering both the spherical wavefront effect and spatial non-stationary (SnS) property. Unlike existing SnS CE methods that rely on the statistical characteristics of channels in the spatial or delay domain, we propose an approach that simultaneously exploits the antenna-domain spatial correlation and the wavenumber-domain sparsity of SnS channels. To this end, we introduce a two-stage VR detection and CE scheme. In the first stage, the belief regarding the visibility of antennas is obtained through a VR detection-oriented message passing (VRDO-MP) scheme, which fully exploits the spatial correlation among adjacent antenna elements. In the second stage, leveraging the VR information and wavenumber-domain sparsity, we accurately estimate the SnS channel employing the belief-based orthogonal matching pursuit (BB-OMP) method. Simulations show that the proposed algorithms lead to a significant enhancement in VR detection and CE accuracy as compared to existing methods, especially in low signal-to-noise ratio (SNR) scenarios., Comment: A major revision version has been submitted to IEEE journal

Systems with 1-bit quantization and oversampling are promising for the Internet of Things (IoT) devices in order to reduce the power consumption of the analog-to-digital-converters. The novel time-instance zero-crossing (TI ZX) modulation is a promising approach for this kind of channels but existing studies rely on optimization problems with high computational complexity and delay. In this work, we propose a practical waveform design based on the established TI ZX modulation for a multiuser multi-input multi-output (MIMO) downlink scenario with 1-bit quantization and temporal oversampling at the receivers. In this sense, the proposed temporal transmit signals are constructed by concatenating segments of coefficients which convey the information into the time-instances of zero-crossings according to the TI ZX mapping rules. The proposed waveform design is compared with other methods from the literature. The methods are compared in terms of bit error rate and normalized power spectral density. Numerical results show that the proposed technique is suitable for multiuser MIMO system with 1-bit quantization while tolerating some small amount of out-of-band radiation.

Low-resolution analog-to-digital converters (ADCs) are promising for reducing energy consumption and costs of multiuser multiple-input multiple-output (MIMO) systems with many antennas. We propose low-resolution multiuser MIMO receivers where the signals are simultaneously processed by 1-bit ADCs and a comparator network, which can be interpreted as additional virtual channels with binary outputs. We distinguish the proposed comparator networks in fully and partially connected. For such receivers, we develop the low-resolution aware linear minimum mean-squared error (LRA-LMMSE) channel estimator and detector according to the Bussgang theorem. We also develop a robust detector which takes into account the channel state information (CSI) mismatch statistics. By exploiting knowledge of the channel coefficients we devise a mean-square error (MSE) greedy search and a sequential signal-to-interference-plus-noise ratio (SINR) search for optimization of partially connected networks. Numerical results show that a system with extra virtual channels can outperform a system with additional receive antennas, in terms of bit error rate (BER). Furthermore, by employing the proposed channel estimation with its error statistics, we construct a lower bound on the ergodic sum rate for a linear receiver. Simulation results confirm that the proposed approach outperforms the conventional 1-bit MIMO system in terms of BER, MSE and sum rate.

The pivoted QLP decomposition is computed through two consecutive pivoted QR decompositions, and provides an approximation to the singular value decomposition. This work is concerned with a partial QLP decomposition of low-rank matrices computed through randomization, termed Randomized Unpivoted QLP (RU-QLP). Like pivoted QLP, RU-QLP is rank-revealing and yet it utilizes random column sampling and the unpivoted QR decomposition. The latter modifications allow RU-QLP to be highly parallelizable on modern computational platforms. We provide an analysis for RU-QLP, deriving bounds in spectral and Frobenius norms on: i) the rank-revealing property; ii) principal angles between approximate subspaces and exact singular subspaces and vectors; and iii) low-rank approximation errors. Effectiveness of the bounds is illustrated through numerical tests. We further use a modern, multicore machine equipped with a GPU to demonstrate the efficiency of RU-QLP. Our results show that compared to the randomized SVD, RU-QLP achieves a speedup of up to 7.1 times on the CPU and up to 2.3 times with the GPU.

In this paper, we propose a general robust subband adaptive filtering (GR-SAF) scheme against impulsive noise by minimizing the mean square deviation under the random-walk model with individual weight uncertainty. Specifically, by choosing different scaling factors such as from the M-estimate and maximum correntropy robust criteria in the GR-SAF scheme, we can easily obtain different GR-SAF algorithms. Importantly, the proposed GR-SAF algorithm can be reduced to a variable regularization robust normalized SAF algorithm, thus having fast convergence rate and low steady-state error. Simulations in the contexts of system identification with impulsive noise and echo cancellation with double-talk have verified that the proposed GR-SAF algorithms outperforms its counterparts., Comment: 15 pages, 17 figures

This paper studies distributed diffusion adaptation over clustered multi-task networks in the presence of impulsive interferences and Byzantine attacks. We develop a robust resilient diffusion least mean Geman-McClure-estimation (RDLMG) algorithm based on the cost function used by the Geman-McClure estimator, which can reduce the sensitivity to large outliers and make the algorithm robust under impulsive interferences. Moreover, the mean sub-sequence reduced method, in which each node discards the extreme value information of cost contributions received from its neighbors, can make the network resilient against Byzantine attacks. In this regard, the proposed RDLMG algorithm ensures that all normal nodes converge to their ideal states with cooperation among nodes. A statistical analysis of the RDLMG algorithm is also carried out in terms of mean and mean-square performances. Numerical results evaluate the proposed RDLMG algorithm in applications to multi-target localization and multi-task spectrum sensing., Comment: 16 pages, 22 figures

This paper proposes a unified sparsity-aware robust normalized subband adaptive filtering (SA-RNSAF) algorithm for identification of sparse systems under impulsive noise. The proposed SA-RNSAF algorithm generalizes different algorithms by defining the robust criterion and sparsity-aware penalty. Furthermore, by alternating optimization of the parameters (AOP) of the algorithm, including the step-size and the sparsity penalty weight, we develop the AOP-SA-RNSAF algorithm, which not only exhibits fast convergence but also obtains low steady-state misadjustment for sparse systems. Simulations in various noise scenarios have verified that the proposed AOP-SA-RNSAF algorithm outperforms existing techniques., Comment: 8 pages, 3 figures

We propose a novel M-estimate conjugate gradient (CG) algorithm, termed Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive noise environments. In particular, the TbMCG algorithm can achieve a faster convergence while retaining a reduced computational complexity as compared to the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight M-estimate incorporates a constraint into the CG filter to tackle impulsive noise environments. Moreover, the convergence behavior of the TbMCG algorithm is analyzed. Simulation results confirm the excellent performance of the proposed TbMCG algorithm for system identification and active noise control applications., Comment: 16 pages, 6 figures

Multiuser multiple-input multiple-output wireless communications systems have the potential to satisfy the performance requirements of fifth-generation and future wireless networks. In this context, cell-free (CF) systems, where the antennas are distributed over the area of interest, have attracted attention because of their potential to enhance the overall efficiency and throughput performance when compared to traditional networks based on cells. However, the performance of CF systems is degraded by imperfect channel state information (CSI). To mitigate the detrimental effects of imperfect CSI, we employ rate splitting (RS) - a multiple-access scheme. The RS approach divides the messages of the users into two separate common and private portions so that interference is managed robustly. Unlike prior works, where the impact of RS in CF systems remains unexamined, we propose a CF architecture that employs RS with linear precoders to address deteriorated CSI. We derive closed-form expressions to compute the sum-rate performance of the proposed RS-CF architecture. Our numerical experiments show that our RS-CF system outperforms existing systems in terms of sum-rate, obtaining up to $10$% higher gain., Comment: 4 figures, 6 pages

Most of the literature focuses on the development of the linear active noise control (ANC) techniques. However, ANC systems might have to deal with some nonlinear components and the performance of linear ANC techniques may degrade in this scenario. To overcome this limitation, nonlinear ANC (NLANC) algorithms were developed. In Part II, we review the development of NLANC algorithms during the last decade. The contributions of heuristic ANC algorithms are outlined. Moreover, we emphasize recent advances of NLANC algorithms, such as spline ANC algorithms, kernel adaptive filters, and nonlinear distributed ANC algorithms. Then, we present recent applications of ANC technique including linear and nonlinear perspectives. Future research challenges regarding ANC techniques are also discussed., Comment: 59 pages, 9 figures

Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)-based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed., Comment: 17 figures, 73 pages

In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean square behaviors of the algorithm, which is supported by simulations. In addition, an adaptive approach for the choice of the thresholding parameter in the proximal step is also proposed based on the minimization of the mean square deviation. Simulations in the contexts of system identification and acoustic echo cancellation verify the superiority of the proposed algorithm over its counterparts., Comment: 12 figures, 13 pages

In this paper, a novel and robust algorithm is proposed for adaptive beamforming based on the idea of reconstructing the autocorrelation sequence (ACS) of a random process from a set of measured data. This is obtained from the first column and the first row of the sample covariance matrix (SCM) after averaging along its diagonals. Then, the power spectrum of the correlation sequence is estimated using the discrete Fourier transform (DFT). The DFT coefficients corresponding to the angles within the noise-plus-interference region are used to reconstruct the noise-plus-interference covariance matrix (NPICM), while the desired signal covariance matrix (DSCM) is estimated by identifying and removing the noise-plus-interference component from the SCM. In particular, the spatial power spectrum of the estimated received signal is utilized to compute the correlation sequence corresponding to the noise-plus-interference in which the dominant DFT coefficient of the noise-plus-interference is captured. A key advantage of the proposed adaptive beamforming is that only little prior information is required. Specifically, an imprecise knowledge of the array geometry and of the angular sectors in which the interferences are located is needed. Simulation results demonstrate that compared with previous reconstruction-based beamformers, the proposed approach can achieve better overall performance in the case of multiple mismatches over a very large range of input signal-to-noise ratios., Comment: 12 pages, 12 figures

This paper investigates the physical layer security design of an untrusted relaying network where the source node coexists with a multi-antenna eavesdropper (Eve). While the communication relies on untrustworthy relay nodes to increase reliability, we aim to protect the confidentiality of information against combined eavesdropping attacks performed by both untrusted relay nodes and Eve. Taking into account the hardware impairments, and power budget constraints, this paper presents a novel approach to jointly optimize relay beamformer and transmit powers aimed at maximizing average secrecy rate (ASR). The resultant optimization problem is non-convex, and a suboptimal solution is obtained through the sequential parametric convex approximation (SPCA) method. In order to prevent any failure due to infeasibility, we propose an iterative initialization algorithm to find the feasible initial point of the original problem. To satisfy low-latency as one of the main key performance indicators (KPI) required in beyond 5G (B5G) communications, a computationally efficient data-driven approach is developed exploiting a deep learning model to improve the ASR while the computational burden is significantly reduced. Simulation results assess the effect of different system parameters on the ASR performance as well as the effectiveness of the proposed deep learning solution in large-scale cases., Comment: 31 pages, 17 figures

In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms., Comment: 17 pages, 12 figures

A channel with continuous phase modulation and 1-bit ADC with oversampling is considered. Due to oversampling, higher-order modulations yield a higher achievable rate and this work presents a method to approach this with sophisticated coding. A tailored bit mapping is proposed where two bits can be detected reliably at high SNR and only the third bit suffers from an error floor. With this, a convolutional code is only applied to the third bit. Our experiments with a turbo receiver show that the proposed method corresponds to a lower BER in comparison to the case of conventional channel coding., Comment: 5 pages, 7 figures, Asilomar 2019

In this paper, we present the concept of non-uniform channel polarization and a scheme for rate-compatible polar code construction for any codeword length using additive white Gaussian noise (AWGN) channels and the successive cancellation (SC) decoder. A Non-Uniform Polarization technique based on the Gaussian Approximation (NUPGA) is developed and an efficient rate-compatible design technique is devised to choose the best channels for transmission by a process of re-polarization of the codeword with the desired length. Simulations illustrate the proposed NUPGA design against existing rate-compatible techniques., Comment: 7 pages, 6 figures

Low-resolution devices are promising for systems that demand low energy consumption and low complexity as required in IoT systems. In this study, we propose a novel waveform for bandlimited channels with 1-bit quantization and oversampling at the receivers. The proposed method implies that the information is conveyed within the time instances of zero-crossings which is then utilized in combination with a Gray-coding scheme. Unlike the existing method, the proposed method does not require optimization and transmission of a dynamic codebook. The proposed approach outperforms the state-of-the-art method in terms of bit error rate., Comment: 5 pages, submitted to ICASSP 2020

Existing reduced-dimension beam-Doppler space-time adaptive processing (RD-BD-STAP) algorithms are confined to the beam-Doppler cells used for adaptation, which often leads to some performance degradation. In this work, a novel sparsity-aware RD-BD-STAP algorithm, denoted Sparse Constraint on Beam-Doppler Selection Reduced-Dimension Space-Time Adaptive Processing (SCBDS-RD-STAP), is proposed can adaptively selects the best beam-Doppler cells for adaptation. The proposed SCBDS-RD-STAP approach formulates the filter design as a sparse representation problem and enforcing most of the elements in the weight vector to be zero (or sufficiently small in amplitude). Simulation results illustrate that the proposed SCBDS-RD-STAP algorithm outperforms the traditional RD-BD-STAP approaches with fixed beam-Doppler localized processing., Comment: 6 figures, 7 pages

Low-rank matrix approximation plays an increasingly important role in signal and image processing applications. This paper presents a new rank-revealing decomposition method called randomized rank-revealing UZV decomposition (RRR-UZVD). RRR-UZVD is powered by randomization to approximate a low-rank input matrix. Given a large and dense matrix ${\bf A} \in \mathbb R^{m \times n}$ whose numerical rank is $k$, where $k$ is much smaller than $m$ and $n$, RRR-UZVD constructs an approximation $\hat{\bf A}$ such as $\hat{\bf A}={\bf UZV}^T$, where ${\bf U}$ and ${\bf V}$ have orthonormal columns, the leading-diagonal block of ${\bf Z}$ reveals the rank of $\bf A$, and its off-diagonal blocks have small $\ell_2$-norms. RRR-UZVD is simple, accurate, and only requires a few passes through $\bf A$ with an arithmetic cost of $O(mnk)$ floating-point operations. To demonstrate the effectiveness of the proposed method, we conduct experiments on synthetic data, as well as real data in applications of image reconstruction and robust principal component analysis.

An efficient, accurate and reliable approximation of a matrix by one of lower rank is a fundamental task in numerical linear algebra and signal processing applications. In this paper, we introduce a new matrix decomposition approach termed Subspace-Orbit Randomized singular value decomposition (SOR-SVD), which makes use of random sampling techniques to give an approximation to a low-rank matrix. Given a large and dense data matrix of size $m\times n$ with numerical rank $k$, where $k \ll \text{min} \{m,n\}$, the algorithm requires a few passes through data, and can be computed in $O(mnk)$ floating-point operations. Moreover, the SOR-SVD algorithm can utilize advanced computer architectures, and, as a result, it can be optimized for maximum efficiency. The SOR-SVD algorithm is simple, accurate, and provably correct, and outperforms previously reported techniques in terms of accuracy and efficiency. Our numerical experiments support these claims.

This paper presents a novel distributed low-rank scheme and adaptive algorithms for distributed estimation over wireless networks. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by transmission of a reduced set of parameters to other agents and reduced-dimension parameter estimation. Distributed low-rank joint iterative estimation algorithms based on alternating optimization strategies are developed, which can achieve significantly reduced communication overhead and improved performance when compared with existing techniques. A computational complexity analysis of the proposed and existing low-rank algorithms is presented along with an analysis of the convergence of the proposed techniques. Simulations illustrate the performance of the proposed strategies in applications of wireless sensor networks and smart grids., Comment: 12 figures, 13 pages. arXiv admin note: text overlap with arXiv:1411.1125

In this paper, we propose sparsity-aware data-selective adaptive filtering algorithms with adjustable penalties. Prior work incorporates a penalty function into the cost function used in the optimization that originates the algorithms to improve their performance by exploiting sparsity. However, the strength of the penalty function is controlled by a scalar that is often a fixed parameter. In contrast to prior work, we develop a framework to derive algorithms that automatically adjust the penalty function parameter and the step size to achieve a better performance. Simulations for a system identification application show that the proposed algorithms outperform in convergence speed existing sparsity-aware algorithms., Comment: 4 figures, 2 tables

This paper presents joint maximum signal-to-interference-plus-noise ratio (MSINR) and relay selection algorithms for distributed beamforming. We propose a joint MSINR and restricted greedy search relay selection (RGSRS) algorithm with a total relay transmit power constraint that iteratively optimizes both the beamforming weights at the relays nodes, maximizing the SINR at the destination. Specifically, we devise a relay selection scheme that based on greedy search and compare it to other schemes like restricted random relay selection (RRRS) and restricted exhaustive search relay selection (RESRS). A complexity analysis is provided and simulation results show that the proposed joint MSINR and RGSRS algorithm achieves excellent bit error rate (BER) and SINR performances., Comment: 7 pages, 2 figures. arXiv admin note: text overlap with arXiv:1707.00953

In this work, we propose buffer-aided physical-layer network coding (PLNC) techniques for cooperative direct-sequence code-division multiple access systems. In the proposed buffer-aided PLNC schemes, a relay pair selection algorithm is employed to obtain the relay pair and the packets in the buffer entries with the best performance and the associated link combinations used for data transmission. We also devise PLNC schemes based on the design of linear network coding matrices using maximum likelihood and minimum mean-square error criteria to generate the network coded symbols that are sent to the destination. Simulation results show that the proposed techniques significantly outperform prior art., Comment: 6 pages, 3 figures

We present a novel sparsity-based space-time adaptive processing (STAP) technique based on the alternating direction method to overcome the severe performance degradation caused by array gain/phase (GP) errors. The proposed algorithm reformulates the STAP problem as a joint optimization problem of the spatio-Doppler profile and GP errors in both single and multiple snapshots, and introduces a target detector using the reconstructed spatio-Doppler profiles. Simulations are conducted to illustrate the benefits of the proposed algorithm., Comment: 7 figures, 1 table

Multiple-antenna systems is a key technique to serve multiple users in future wireless systems. For low energy consumption and hardware complexity we first consider transmit symbols with constant magnitude and then 1-bit digital-to-analog converters. We propose precoding designs which maximize the minimum distance to the decision threshold at the receiver. The precoding design with 1-bit DAC corresponds to a discrete optimization problem, which we solve exactly with a branch-and-bound strategy. We alternatively present an approximation based on relaxation. Our results show that the proposed branch-and-bound approach has polynomial complexity. The proposed methods outperform existing precoding methods with 1-bit DAC in terms of uncoded bit error rate and sum-rate. The performance loss in comparison to infinite DAC resolution is small., Comment: 4 pages, 3 figures

In this paper we study widely-linear precoding techniques to mitigate in-phase/quadrature-phase (IQ) imbalance (IQI) in the downlink of large-scale multiple-input multiple-output (MIMO) systems. We adopt a real-valued signal model which takes into account the IQI at the transmitter and then develop widely-linear zero-forcing (WL-ZF), widely-linear matched filter (WL-MF), widely-linear minimum mean-squared error (WL-MMSE) and widely-linear block-diagonalization (WL-BD) type precoding algorithms for both {\color{red} single- and multiple-antenna users.} We also present a performance analysis of WL-ZF and WL-BD. It is proved that without IQI, WL-ZF has exactly the same multiplexing gain and power offset as ZF, while when IQI exists, WL-ZF achieves the same multiplexing gain as ZF with ideal IQ branches, but with a minor power loss which is related to the system scale and the IQ parameters. We also compare the performance of WL-BD with BD. The analysis shows that with ideal IQ branches, WL-BD has the same data rate as BD, while when IQI exists, WL-BD achieves the same multiplexing gain as BD without IQ imbalance. Numerical results verify the analysis and show that the proposed widely-linear type precoding methods significantly outperform their conventional counterparts with IQI and approach those with ideal IQ branches., Comment: Accepted in IEEE TWC

In this work, we propose an alternating low-rank decomposition (ALRD) approach and novel subspace algorithms for direction-of-arrival (DOA) estimation. In the ALRD scheme, the decomposition matrix for rank reduction is composed of a set of basis vectors. A low-rank auxiliary parameter vector is then employed to compute the output power spectrum. Alternating optimization strategies based on recursive least squares (RLS), denoted as ALRD-RLS and modified ALRD-RLS (MARLD-RLS), are devised to compute the basis vectors and the auxiliary parameter vector. Simulations for large sensor arrays with both uncorrelated and correlated sources are presented, showing that the proposed algorithms are superior to existing techniques., Comment: 3 figures, 11 pages

This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We present sparsity-aware conventional and modified distributed CG algorithms using $l_{1}$ and log-sum penalty functions. The proposed sparsity-aware diffusion distributed CG algorithms have an improved performance in terms of mean square deviation (MSD) and convergence as compared with the consensus least-mean square (Diffusion-LMS) algorithm, the diffusion CG algorithms and a close performance to the diffusion distributed recursive least squares (Consensus-RLS) algorithm. Numerical results show that the proposed algorithms are reliable and can be applied in several scenarios., Comment: 1 figure, 7 pages

We present a tutorial on reduced-rank signal processing, design methods and algorithms for dimensionality reduction, and cover a number of important applications. A general framework based on linear algebra and linear estimation is employed to introduce the reader to the fundamentals of reduced-rank signal processing and to describe how dimensionality reduction is performed on an observed discrete-time signal. A unified treatment of dimensionality reduction algorithms is presented with the aid of least squares optimization techniques, in which several techniques for designing the transformation matrix that performs dimensionality reduction are reviewed. Among the dimensionality reduction techniques are those based on the eigen-decomposition of the observed data vector covariance matrix, Krylov subspace methods, joint and iterative optimization (JIO) algorithms and JIO with simplified structures and switching (JIOS) techniques. A number of applications are then considered using a unified treatment, which includes wireless communications, sensor and array signal processing, and speech, audio, image and video processing. This tutorial concludes with a discussion of future research directions and emerging topics., Comment: 23 pages, 6 figures

In this work, we consider the use of buffer-aided relays, linear precoding techniques and multiple antennas for physical-layer security in wireless networks. We develop relay selection algorithms to improve the secrecy-rate performance of cooperative multi-user multiple-antenna wireless networks. In particular, we propose a novel finite buffer-aided relay selection algorithm that employs the maximum likelihood (ML) criterion to select sets of relays which fully exploit the flexibility offered by relay nodes equipped with buffers. Numerical results show the benefits of the proposed techniques as compared to prior art., Comment: 5 pages, 3 figures in WSA 2015

We propose iterative detection and decoding (IDD) algorithms with Low-Density Parity-Check (LDPC) codes for Multiple Input Multiple Output (MIMO) systems operating in block-fading and fast Rayleigh fading channels. Soft-input soft-output minimum mean-square error receivers with successive interference cancellation are considered. In particular, we devise a novel strategy to improve the bit error rate (BER) performance of IDD schemes, which takes into account the soft \textit{a posteriori} output of the decoder in a block-fading channel when Root-Check LDPC codes are used. A MIMO IDD receiver with soft information processing that exploits the code structure and the behavior of the log likelihood ratios is also developed. Moreover, we present a scheduling algorithm for decoding LDPC codes in block-fading channels. Simulations show that the proposed techniques result in significant gains in terms of BER for both block-fading and fast-fading channels., Comment: 17 pages, 4 figures, IEEE Transactions on Vehicular Technology, 2015

An adaptive delay-tolerant distributed space-time coding (DSTC) scheme that exploits feedback is proposed for two-hop cooperative MIMO networks. Maximum likelihood (ML) receivers and adjustable code matrices are considered subject to a power constraint with a decode-and-forward (DF) cooperation strategy. In the proposed delay-tolerant DSTC scheme, an adjustable code matrix is employed to transform the space-time coded matrix at the relay nodes. Least-squares (LS) algorithms are developed with reduced computational complexity to adjust the parameters of the codes. Simulation results show that the proposed algorithms obtain significant performance gains and address the delay issue for cooperative MIMO systems as compared to existing delay-tolerant DSTC schemes., Comment: 4 figures