Sparse multiple relay selection for network beamforming with individual power constraints using semidefinite relaxation

This paper deals with the multiple relay selection problem in two-hop wireless cooperative networks with individual power constraints at the relays. In particular, it addresses the problem of selecting the best subset of K cooperative nodes and their corresponding beamforming weights so that the signal-to-noise ratio (SNR) is maximized at the destination. This problem is computationally demanding and requires an exhaustive search over all the possible combinations. In order to reduce the complexity, a new suboptimal method is proposed. This technique exhibits a near-optimal performance with a computational burden that is far less than the one needed in the combinatorial search. The proposed method is based on the use of the l1-norm squared and the Charnes-Cooper transformation and naturally leads to a semidefinite programming relaxation with an affordable computational cost. Contrary to other approaches in the literature, the technique exposed herein is based on the knowledge of the second-order statistics of the channels and the relays are not limited to cooperate with full power.Peer ReviewedPostprint (author's final draft

Exploiting Sparsity in Amplify-and-Forward Broadband Multiple Relay Selection

Cooperative communication has attracted significant attention in the last decade due to its ability to increase the spatial diversity order with only single-antenna nodes. However, most of the techniques in the literature are not suitable for large cooperative networks such as device-to-device and wireless sensor networks that are composed of a massive number of active devices, which significantly increases the relay selection complexity. Therefore, to solve this problem and enhance the spatial and frequency diversity orders of large amplify and forward cooperative communication networks, in this paper, we develop three multiple relay selection and distributed beamforming techniques that exploit sparse signal recovery theory to process the subcarriers using the low complexity orthogonal matching pursuit algorithm (OMP). In particular, by separating all the subcarriers or some subcarrier groups from each other and by optimizing the selection and beamforming vector(s) using OMP algorithm, a higher level of frequency diversity can be achieved. This increased diversity order allows the proposed techniques to outperform existing techniques in terms of bit error rate at a lower computation complexity. A detailed performance-complexity tradeoff, as well as Monte Carlo simulations, are presented to quantify the performance and efficiency of the proposed techniques. 2013 IEEE.This publication was made possible by NPRP grant 8-627-2-260 and NPRP grant 6-070-2-024 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Advanced DSP Algorithms For Modern Wireless Communication Transceivers

A higher network throughput, a minimized delay and reliable communications are some of many goals that wireless communication standards, such as the fifthgeneration (5G) standard and beyond, intend to guarantee for its customers. Hence, many key innovations are currently being proposed and investigated by researchers in the academic and industry circles to fulfill these goals. This dissertation investigates some of the proposed techniques that aim at increasing the spectral efficiency, enhancing the energy efficiency, and enabling low latency wireless communications systems. The contributions lay in the evaluation of the performance of several proposed receiver architectures as well as proposing novel digital signal processing (DSP) algorithms to enhance the performance of radio transceivers. Particularly, the effects of several radio frequency (RF) impairments on the functionality of a new class of wireless transceivers, the full-duplex transceivers, are thoroughly investigated. These transceivers are then designed to operate in a relaying scenario, where relay selection and beamforming are applied in a relaying network to increase its spectral efficiency. The dissertation then investigates the use of greedy algorithms in recovering orthogonal frequency division multiplexing (OFDM) signals by using sparse equalizers, which carry out the equalization in a more efficient manner when the low-complexity single tap OFDM equalizer can no longer recover the received signal due to severe interferences. The proposed sparse equalizers are shown to perform close to conventional optimal and dense equalizers when the OFDM signals are impaired by interferences caused by the insertion of an insufficient cyclic prefix and RF impairments

Performance Analysis of Output Threshold-Based Incremental Multiple-Relay Combining Scheme with Adaptive Modulation for Cooperative Networks

In this paper, we propose an output threshold-based incremental multiple-relay combining scheme for cooperative amplify-and-forward relay networks with nonidentically distributed relay channels. Specifically, in order to achieve the required performance, we consider both conventional incremental relaying and multiple-relay selection where relays are adaptively selected based on a predetermined output threshold. Moreover, the adaptive modulation technique is adopted by our proposed scheme for satisfying both the spectral efficiency and the required error rate. For the proposed scheme, we first derive an upper bound of the output combined signal-to-noise ratio and then provide its statistics such as cumulative distribution function (CDF), probability density function (PDF), and moment generating function (MGF) over independent, nonidentically distributed Rayleigh fading channels. Additionally, we analyze the system performance in terms of average spectral efficiency, average bit error rate, outage probability, and system complexity. Finally, numerical examples show that our proposed scheme leads to a certain performance improvement in the cooperative networks

A Novel Antenna Selection Scheme for Spatially Correlated Massive MIMO Uplinks with Imperfect Channel Estimation

We propose a new antenna selection scheme for a massive MIMO system with a single user terminal and a base station with a large number of antennas. We consider a practical scenario where there is a realistic correlation among the antennas and imperfect channel estimation at the receiver side. The proposed scheme exploits the sparsity of the channel matrix for the effective selection of a limited number of antennas. To this end, we compute a sparse channel matrix by minimising the mean squared error. This optimisation problem is then solved by the well-known orthogonal matching pursuit algorithm. Widely used models for spatial correlation among the antennas and channel estimation errors are considered in this work. Simulation results demonstrate that when the impacts of spatial correlation and imperfect channel estimation introduced, the proposed scheme in the paper can significantly reduce complexity of the receiver, without degrading the system performance compared to the maximum ratio combining.Comment: in Proc. IEEE 81st Vehicular Technology Conference (VTC), May 2015, 6 pages, 5 figure

Hybrid Full-Duplex and Alternate Multiple Relay Selection and Beamforming in AF Cooperative Networks

In this paper, multiple relay selection and beamforming techniques are applied to a dual-hop full-duplex (FD) amplify-and-forward relaying network. We show that our proposed techniques allow the selection to be adaptive to the residual self-interference (SI) level for each of the available relays in the network. The adaptivity of our selection schemes is manifested through a hybrid system that is based on FD relaying and switching based on the overall channel conditions and the statistics of the residual SI channel for each of the relays. In particular, different proposed techniques are shown to be able to adaptively decide on when and how often the used relays should be switched in the case of overwhelming residual SI. Our results show that allowing such a fusion considerably improves the overall performance of the considered relaying scheme in terms of bit error rate compared with state-of-the-art relay selection schemes.This work was supported by the Qatar National Research Fund (a member of the Qatar Foundation) through GSRA under Grant #2-1-0601-14011.Scopu

Sparsity-aware multiple relay selection in large multi-hop decode-and-forward relay networks

In this paper, we propose and investigate two novel techniques to perform multiple relay selection in large multi-hop decode-and-forward relay networks. The two proposed techniques exploit sparse signal recovery theory to select multiple relays using the orthogonal matching pursuit algorithm and outperform state-of-the-art techniques in terms of outage probability and computation complexity. To reduce the amount of collected channel state information (CSI), we propose a limited-feedback scheme where only a limited number of relays feedback their CSI. Furthermore, a detailed performance-complexity tradeoff investigation is conducted for the different studied techniques and verified by Monte Carlo simulations.NPRP grant 6-070-2-024 from the Qatar National Research Fund (a member of Qatar Foundation)Scopu

Soft information based protocols in network coded relay networks

Future wireless networks aim at providing higher quality of service (QoS) to mobile users. The emergence of relay technologies has shed light on new methodologies through which the system capacity can be dramatically increased with low deployment cost. In this thesis, novel relay technologies have been proposed in two practical scenarios: wireless sensor networks (WSN) and cellular networks. In practical WSN designs, energy conservation is the single most important requirement. This thesis draws attention to a multiple access relay channels model in the WSN. The network coded symbol for the received signals from correlated sources has been derived; the network coded symbol vector is then converted into a sparse vector, after which a compressive sensing (CS) technique is applied over the sparse signals. A theoretical proof analysis is derived regarding the reliability of the network coded symbol formed in the proposed protocol. The proposed protocol results in a better bit error rate (BER) performance in comparison to the direct implementation of CS on the EF protocol. Simulation results validate our analyses. Another hot topic is the application of relay technologies to the cellular networks. In this thesis, a practical two-way transmission scheme is proposed based on the EF protocol and the network coding technique. A trellis coded quantization/modulation (TCQ/M) scheme is used in the network coding process. The soft network coded symbols are quantized into only one bit thus requiring the same transmission bandwidth as the simplest decode-and-forward protocol. The probability density function of the network coded symbol is derived to help to form the quantization codebook for the TCQ. Simulations show that the proposed soft forwarding protocol can achieve full diversity with only a transmission rate of 1, and its BER performance is equivalent to that of an unquantized EF protocol