Linear Reduced-Rank Interference Suppression for DS-UWB Systems Using Switched Approximations of Adaptive Basis Functions

In this work, we propose a novel low-complexity reduced-rank scheme and consider its application to linear interference suppression in direct-sequence ultra-wideband (DS-UWB) systems. Firstly, we investigate a generic reduced-rank scheme that jointly optimizes a projection vector and a reduced-rank filter by using the minimum mean-squared error (MMSE) criterion. Then a low-complexity scheme, denoted switched approximation of adaptive basis functions (SAABF), is proposed. The SAABF scheme is an extension of the generic scheme, in which the complexity reduction is achieved by using a multi-branch framework to simplify the structure of the projection vector. Adaptive implementations for the SAABF scheme are developed by using least-mean squares (LMS) and recursive least-squares (RLS) algorithms. We also develop algorithms for selecting the branch number and the model order of the SAABF scheme. Simulations show that in the scenarios with severe inter-symbol interference (ISI) and multiple access interference (MAI), the proposed SAABF scheme has fast convergence and remarkable interference suppression performance with low complexity.Comment: 9 figures. arXiv admin note: text overlap with arXiv:1305.297

Low-Rank Signal Processing: Design, Algorithms for Dimensionality Reduction and Applications

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 figure

Flexible Widely-Linear Multi-Branch Decision Feedback Detection Algorithms for Massive MIMO Systems

This paper presents widely-linear multi-branch decision feedback detection techniques for large-scale multiuser multiple-antenna systems. We consider a scenario with impairments in the radio-frequency chain in which the in-phase (I) and quadrature (Q) components exhibit an imbalance, which degrades the receiver performance and originates non-circular signals. A widely-linear multi-branch decision feedback receiver is developed to mitigate both the multiuser interference and the I/Q imbalance effects. An iterative detection and decoding scheme with the proposed receiver and convolutional codes is also devised. Simulation results show that the proposed techniques outperform existing algorithms.Comment: 3 figures, 9 pages. arXiv admin note: text overlap with arXiv:1308.272

Adaptive Minimum BER Reduced-Rank Linear Detection for Massive MIMO Systems

In this paper, we propose a novel adaptive reduced-rank strategy for very large multiuser multi-input multi-output (MIMO) systems. The proposed reduced-rank scheme is based on the concept of joint iterative optimization (JIO) of filters according to the minimization of the bit error rate (BER) cost function. The proposed optimization technique adjusts the weights of a projection matrix and a reduced-rank filter jointly. We develop stochastic gradient (SG) algorithms for their adaptive implementation and introduce a novel automatic rank selection method based on the BER criterion. Simulation results for multiuser MIMO systems show that the proposed adaptive algorithms significantly outperform existing schemes.Comment: 6 figures. arXiv admin note: substantial text overlap with arXiv:1302.413

Sparsity-Based STAP Design Based on Alternating Direction Method with Gain/Phase Errors

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 tabl

Study of BEM-Type Channel Estimation Techniques for 5G Multicarrier Systems

In this paper, we investigate channel estimation techniques for 5G multicarrier systems. Due to the characteristics of the 5G application scenarios, channel estimation techniques have been tested in Orthogonal Frequency Division Multiplexing (OFDM) and Generalized Frequency Division Multiplexing (GFDM) systems. The orthogonality between subcarriers in OFDM systems permits inserting and extracting pilots without interference. However, due to pulse shaping, subcarriers in GFDM are no longer orthogonal and interfere with each other. Due to such interference, the channel estimation for GFDM is not trivial. A robust and low-complexity channel estimator can be obtained by combining a minimum mean-square error (MMSE) regularization and the basis expansion model (BEM) approach. In this work, we develop a BEM-type channel estimator along with a strategy to obtain the covariance matrix of the BEM coefficients. Simulations show that the BEM-type channel estimation shows performance close to that of the linear MMSE (LMMSE), even though there is no need to know the channel power delay profile, and its complexity is low.Comment: 2 figures, 7 page

Study of Efficient Robust Adaptive Beamforming Algorithms Based on Shrinkage Techniques

This paper proposes low-complexity robust adaptive beamforming (RAB) techniques based on shrinkage methods. We firstly briefly review a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) batch algorithm to estimate the desired signal steering vector mismatch, in which the interference-plus-noise covariance (INC) matrix is also estimated with a recursive matrix shrinkage method. Then we develop low complexity adaptive robust version of the conjugate gradient (CG) algorithm to both estimate the steering vector mismatch and update the beamforming weights. A computational complexity study of the proposed and existing algorithms is carried out. Simulations are conducted in local scattering scenarios and comparisons to existing RAB techniques are provided.Comment: 9 pages, 2 figures. arXiv admin note: text overlap with arXiv:1505.0678

Study of Opportunistic Cooperation Techniques using Jamming and Relays for Physical-Layer Security in Buffer-aided Relay Networks

In this paper, we investigate opportunistic relay and jammer cooperation schemes in multiple-input multiple-output (MIMO) buffer-aided relay networks. The network consists of one source, an arbitrary number of relay nodes, legitimate users and eavesdroppers, with the constraints of physical layer security. We propose an algorithm to select a set of relay nodes to enhance the legitimate users' transmission and another set of relay nodes to perform jamming of the eavesdroppers. With Inter-Relay interference (IRI) taken into account, interference cancellation can be implemented to assist the transmission of the legitimate users. Secondly, IRI can also be used to further increase the level of harm of the jamming signal to the eavesdroppers. By exploiting the fact that the jamming signal can be stored at the relay nodes, we also propose a hybrid algorithm to set a signal-to-interference and noise ratio (SINR) threshold at the node to determine the type of signal stored at the relay node. With this separation, the signals with high SINR are delivered to the users as conventional relay systems and the low SINR performance signals are stored as potential jamming signals. Simulation results show that the proposed techniques obtain a significant improvement in secrecy rate over previously reported algorithms.Comment: 8 pages, 3 figure

Adaptive Reduced-Rank Minimum Symbol-Error-Rate Receive Processing for Large-Scale Multiple-Antenna Systems

In this work, we propose a novel adaptive reduced-rank receive processing strategy based on joint preprocessing, decimation and filtering (JPDF) for large-scale multiple-antenna systems. In this scheme, a reduced-rank framework is employed for linear receive processing and multiuser interference suppression based on the minimization of the symbol-error-rate (SER) cost function. We present a structure with multiple processing branches that performs a dimensionality reduction, where each branch contains a group of jointly optimized preprocessing and decimation units, followed by a linear receive filter. We then develop stochastic gradient (SG) algorithms to compute the parameters of the preprocessing and receive filters, along with a low-complexity decimation technique for both binary phase shift keying (BPSK) and $M$-ary quadrature amplitude modulation (QAM) symbols. In addition, an automatic parameter selection scheme is proposed to further improve the convergence performance of the proposed reduced-rank algorithms. Simulation results are presented for time-varying wireless environments and show that the proposed JPDF minimum-SER receive processing strategy and algorithms achieve a superior performance than existing methods with a reduced computational complexity.Comment: 16 pages, 13 figures, IEEE Transactions on Communications, 201

Distributed Low-Rank Adaptive Algorithms Based on Alternating Optimization and Applications

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:1411.112