Low-Complexity Robust Adaptive Beamforming Algorithms Based on Shrinkage for Mismatch Estimation

In this paper, we propose low-complexity robust adaptive beamforming (RAB) techniques that based on shrinkage methods. The only prior knowledge required by the proposed algorithms are the angular sector in which the actual steering vector is located and the antenna array geometry. We firstly present a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm to estimate the desired signal steering vector mismatch, in which the interference-plus-noise covariance (INC) matrix is estimated with Oracle Approximating Shrinkage (OAS) method and the weights are computed with matrix inversions. We then develop low-cost stochastic gradient (SG) recursions to estimate the INC matrix and update the beamforming weights, resulting in the proposed LOCSME-SG algorithm. Simulation results show that both LOCSME and LOCSME-SG achieve very good output signal-to-interference-plus-noise ratio (SINR) compared to previously reported adaptive RAB algorithms.Comment: 8 pages, 2 figures, WSA. arXiv admin note: text overlap with arXiv:1311.233

Robust Designs of Beamforming and Power Splitting for Distributed Antenna Systems with Wireless Energy Harvesting

In this paper, we investigate a multiuser distributed antenna system with simultaneous wireless information and power transmission under the assumption of imperfect channel state information (CSI). In this system, a distributed antenna port with multiple antennas supports a set of mobile stations who can decode information and harvest energy simultaneously via a power splitter. To design robust transmit beamforming vectors and the power splitting (PS) factors in the presence of CSI errors, we maximize the average worst-case signal-to-interference-plus- noise ratio (SINR) while achieving individual energy harvesting constraint for each mobile station. First, we develop an efficient algorithm to convert the max-min SINR problem to a set of "dual" min-max power balancing problems. Then, motivated by the penalty function method, an iterative algorithm based on semi-definite programming (SDP) is proposed to achieve a local optimal rank-one solution. Also, to reduce the computational complexity, we present another iterative scheme based on the Lagrangian method and the successive convex approximation (SCA) technique to yield a suboptimal solution. Simulation results are shown to validate the robustness and effectiveness of the proposed algorithms.Comment: To appear in IEEE Systems Journal. (10 pages, 6 figures

Low-Complexity Robust Data-Adaptive Dimensionality Reduction Based on Joint Iterative Optimization of Parameters

This paper presents a low-complexity robust data-dependent dimensionality reduction based on a modified joint iterative optimization (MJIO) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank-reduction matrix and an adaptive beamformer. The optimized rank-reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced-dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust MJIO design. The proposed robust MJIO beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed MJIO algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity.Comment: 5 pages, 3 figures. CAMSAP 201

Coordinate Tomlinson-Harashima Precoding Design for Overloaded Multi-user MIMO Systems

Tomlinson-Harashima precoding (THP) is a nonlinear processing technique employed at the transmit side to implement the concept of dirty paper coding (DPC). The perform of THP, however, is restricted by the dimensionality constraint that the number of transmit antennas has to be greater or equal to the total number of receive antennas. In this paper, we propose an iterative coordinate THP algorithm for the scenarios in which the total number of receive antennas is larger than the number of transmit antennas. The proposed algorithm is implemented on two types of THP structures, the decentralized THP (dTHP) with diagonal weighted filters at the receivers of the users, and the centralized THP (cTHP) with diagonal weighted filter at the transmitter. Simulation results show that a much better bit error rate (BER) and sum-rate performances can be achieved by the proposed iterative coordinate THP compared to the previous linear art.Comment: 3 figures, 6 pages, ISWCS 2014. arXiv admin note: text overlap with arXiv:1401.475

Joint Transceiver Design Algorithms for Multiuser MISO Relay Systems with Energy Harvesting

In this paper, we investigate a multiuser relay system with simultaneous wireless information and power transfer. Assuming that both base station (BS) and relay station (RS) are equipped with multiple antennas, this work studies the joint transceiver design problem for the BS beamforming vectors, the RS amplify-and-forward transformation matrix and the power splitting (PS) ratios at the single-antenna receivers. Firstly, an iterative algorithm based on alternating optimization (AO) and with guaranteed convergence is proposed to successively optimize the transceiver coefficients. Secondly, a novel design scheme based on switched relaying (SR) is proposed that can significantly reduce the computational complexity and overhead of the AO based designs while maintaining a similar performance. In the proposed SR scheme, the RS is equipped with a codebook of permutation matrices. For each permutation matrix, a latent transceiver is designed which consists of BS beamforming vectors, optimally scaled RS permutation matrix and receiver PS ratios. For the given CSI, the optimal transceiver with the lowest total power consumption is selected for transmission. We propose a concave-convex procedure based and subgradient-type iterative algorithms for the non-robust and robust latent transceiver designs. Simulation results are presented to validate the effectiveness of all the proposed algorithms

Robust Beamforming for Secrecy Rate in Cooperative Cognitive Radio Multicast Communications

In this paper, we propose a cooperative approach to improve the security of both primary and secondary systems in cognitive radio multicast communications. During their access to the frequency spectrum licensed to the primary users, the secondary unlicensed users assist the primary system in fortifying security by sending a jamming noise to the eavesdroppers, while simultaneously protect themselves from eavesdropping. The main objective of this work is to maximize the secrecy rate of the secondary system, while adhering to all individual primary users' secrecy rate constraints. In the case of passive eavesdroppers and imperfect channel state information knowledge at the transceivers, the utility function of interest is nonconcave and involved constraints are nonconvex, and thus, the optimal solutions are troublesome. To address this problem, we propose an iterative algorithm to arrive at a local optimum of the considered problem. The proposed iterative algorithm is guaranteed to achieve a Karush-Kuhn-Tucker solution.Comment: 6 pages, 4 figures, IEEE ICC 201

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

Sidelobe Suppression for Capon Beamforming with Mainlobe to Sidelobe Power Ratio Maximization

High sidelobe level is a major disadvantage of the Capon beamforming. To suppress the sidelobe, this paper introduces a mainlobe to sidelobe power ratio constraint to the Capon beamforming. it minimizes the sidelobe power while keeping the mainlobe power constant. Simulations show that the obtained beamformer outperforms the Capon beamformer.Comment: 8 pages, 2 figure

Multi-User Flexible Coordinated Beamforming using Lattice Reduction for Massive MIMO Systems

The application of precoding algorithms in multi-user massive multiple-input multiple-output (MU-Massive-MIMO) systems is restricted by the dimensionality constraint that the number of transmit antennas has to be greater than or equal to the total number of receive antennas. In this paper, a lattice reduction (LR)-aided flexible coordinated beamforming (LR-FlexCoBF) algorithm is proposed to overcome the dimensionality constraint in overloaded MU-Massive-MIMO systems. A random user selection scheme is integrated with the proposed LR-FlexCoBF to extend its application to MU-Massive-MIMO systems with arbitary overloading levels. Simulation results show that significant improvements in terms of bit error rate (BER) and sum-rate performances can be achieved by the proposed LR-FlexCoBF precoding algorithm.Comment: 5 figures, Eusipc

Robust Downlink Beamforming in Multiuser MISO Cognitive Radio Networks

This paper studies the problem of robust downlink beamforming design in a multiuser Multi-Input Single-Output (MISO) Cognitive Radio Network (CR-Net) in which multiple Primary Users (PUs) coexist with multiple Secondary Users (SUs). Unlike conventional designs in CR-Nets, in this paper it is assumed that the Channel State Information (CSI) for all relevant channels is imperfectly known, and the imperfectness of the CSI is modeled using an Euclidean ball-shaped uncertainty set. Our design objective is to minimize the transmit power of the SU-Transmitter (SU-Tx) while simultaneously targeting a lower bound on the received Signal-to-Interference-plus-Noise-Ratio (SINR) for the SU's, and imposing an upper limit on the Interference-Power (IP) at the PUs. The design parameters at the SU-Tx are the beamforming weights, i.e. the precoder matrix. The proposed methodology is based on a worst case design scenario through which the performance metrics of the design are immune to variations in the channels. We propose three approaches based on convex programming for which efficient numerical solutions exist. Finally, simulation results are provided to validate the robustness of the proposed methods.Comment: 23 pages, 4 figures, submitted to IEEE Trans. Wireless Comms., accepted conference version to PIMRC'0