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

Study of Robust Distributed Beamforming Based on Cross-Correlation and Subspace Projection Techniques

In this work, we present a novel robust distributed beamforming (RDB) approach to mitigate the effects of channel errors on wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output. The proposed RDB method, denoted cross-correlation and subspace projection (CCSP) RDB, considers a total relay transmit power constraint in the system and the objective of maximizing the output signal-to-interference-plus-noise ratio (SINR). The relay nodes are equipped with an amplify-and-forward (AF) protocol and we assume that the channel state information (CSI) is imperfectly known at the relays and there is no direct link between the sources and the destination. The CCSP does not require any costly optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.Comment: 3 figures, 7 pages. arXiv admin note: text overlap with arXiv:1707.00953

Joint Iterative Power Allocation and Linear Interference Suppression Algorithms in Cooperative DS-CDMA Networks

This work presents joint iterative power allocation and interference suppression algorithms for spread spectrum networks which employ multiple hops and the amplify-and-forward cooperation strategy for both the uplink and the downlink. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression. We derive constrained linear minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the linear receivers. In order to solve the proposed optimization problems, we develop cost-effective algorithms for adaptive joint power allocation, and estimation of the parameters of the receiver and the channels. An analysis of the optimization problem is carried out and shows that the problem can have its convexity enforced by an appropriate choice of the power constraint parameter, which allows the algorithms to avoid problems with local minima. A study of the complexity and the requirements for feedback channels of the proposed algorithms is also included for completeness. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing non-cooperative and cooperative schemes.Comment: 9 figures; IET Communications, 201

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 Joint MSINR and Relay Selection Algorithms for Distributed Beamforming

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:1707.0095

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

Study of Distributed Conjugate Gradient Strategies for Distributed Estimation Over Sensor Networks

This paper presents distributed conjugate gradient algorithms for distributed parameter estimation and spectrum estimation over wireless sensor networks. In particular, distributed conventional conjugate gradient (CCG) and modified conjugate gradient (MCG) are considered, together with incremental and diffusion adaptive solutions. The distributed CCG and MCG algorithms have an improved performance in terms of mean square error as compared with least--mean square (LMS)--based algorithms and a performance that is close to recursive least--squares (RLS) algorithms. In comparison with existing centralized or distributed estimation strategies, key features of the proposed algorithms are: 1) more accurate estimates and faster convergence speed can be obtained; 2) the design of preconditioners for CG algorithms, which have the ability to improve the performance of the proposed CG algorithms is presented and 3) the proposed algorithms are implemented in the area of distributed parameter estimation and spectrum estimation. The performance of the proposed algorithms for distributed estimation is illustrated via simulations and the resulting algorithms are distributed, cooperative and able to respond in real time to change in the environment.Comment: 23 pages, 10 figure

Compression and Combining Based on Channel Shortening and Rank Reduction Techniques for Cooperative Wireless Sensor Networks

This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of $L$ sensors. As the destination has the capacity of processing only $U$ out of these $L$ signals, the strongest $U$ signals are selected while the remaining $(L-U)$ signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.Comment: In IEEE Transactions on Vehicular Technology, 201

Alternating Optimization Techniques for Power Allocation and Receiver Design in Multihop Wireless Sensor Networks

In this paper, we consider a multihop wireless sensor network with multiple relay nodes for each hop where the amplify-and-forward scheme is employed. We present algorithmic strategies to jointly design linear receivers and the power allocation parameters via an alternating optimization approach subject to different power constraints which include global, local and individual ones. Two design criteria are considered: the first one minimizes the mean-square error and the second one maximizes the sum-rate of the wireless sensor network. We derive constrained minimum mean-square error and constrained maximum sum-rate expressions for the linear receivers and the power allocation parameters that contain the optimal complex amplification coefficients for each relay node. An analysis of the computational complexity and the convergence of the algorithms is also presented. Computer simulations show good performance of our proposed methods in terms of bit error rate and sum-rate compared to the method with equal power allocation and an existing power allocation scheme.Comment: 10 figures, 13 pages. IEEE Transactions on Vehicular Technology, 2014. arXiv admin note: text overlap with arXiv:1303.384

Interference Suppression in Multiuser Systems Based on Bidirectional Algorithms

This paper presents adaptive bidirectional minimum mean-square error parameter estimation algorithms for fast-fading channels. The time correlation between successive channel gains is exploited to improve the estimation and tracking capabilities of adaptive algorithms and provide robustness against time-varying channels. Bidirectional normalized least mean-square and conjugate gradient algorithms are devised along with adaptive mixing parameters that adjust to the time-varying channel correlation properties. An analysis of the proposed algorithms is provided along with a discussion of their performance advantages. Simulations for an application to interference suppression in multiuser DS-CDMA systems show the advantages of the proposed algorithms.Comment: 11 figures, Eurasip journal on wireless communications and networking, 201