Optimizations of a MIMO Relay Network

Behbahani, Alireza S.; Merched, R.; Eltawil, Ahmed M.

doi:10.1109/tsp.2008.929120

Cited by 240 publications

(218 citation statements)

References 23 publications

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“…2. BER performance comparisons of the proposed = 2 noncooperative distributed relay networks and the existing ones in [6], [7], and [8] under a global power constraint. Fig.…”

Section: Noncooperative Mmse Relay Scheme Under Local Power Constmentioning

confidence: 99%

“…And, under this assumption, multiple approximations were used in [6]. Behbahani and Eltawil in [7] minimized the mean square error cost under the power constraint at an equalizer input. Recently, the authors in [8] derived an optimal relay factor for the one-source-one-destination pair and -relay node network to minimize MSE under the power constraint at the destination node.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the authors in [8] derived an optimal relay factor for the one-source-one-destination pair and -relay node network to minimize MSE under the power constraint at the destination node. However, Behbahani et al in [7] and Behbahani and Eltawil in [8] employed the proportionality to solve the optimization problem as shown in (17) of [7] and in (15) of [8], respectively. However, in [9], [10], noncooperative distributed minimum mean square error (MMSE)-based AF relay schemes in wireless networks under the received power constraint at the destination node for single-input single-output with relay nodes was studied by the authors of this paper.…”

Section: Introductionmentioning

confidence: 99%

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MMSE-Based amplifying relay matrix for noncooperative AF wireless relay network under power constraints

Lee

Kwon

Wang

2011

2011 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-This paper derives analytically an optimum amplifying relay matrix using the minimum mean square error (MMSE) criteria for a noncooperative amplify-and-forward (AF) distributed relay network. Global and local power constraints are included in the analysis. And a one-source-one-destination node pair and -relay network is considered. Because the relays are noncooperative, this paper exploits the diagonal property in an amplifying relay matrix for the optimum matrix derivation. Then, this paper claims that the bit error rate of the AF network using the proposed amplifying relay matrix is significantly better than that of the AF network with the other existing amplifying matrices.Index Terms-Amplify-and-forward, minimum mean square error, amplifying relay matrix, power constraint.

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“…2. BER performance comparisons of the proposed = 2 noncooperative distributed relay networks and the existing ones in [6], [7], and [8] under a global power constraint. Fig.…”

Section: Noncooperative Mmse Relay Scheme Under Local Power Constmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MMSE-Based amplifying relay matrix for noncooperative AF wireless relay network under power constraints

Lee

Kwon

Wang

2011

2011 IEEE Symposium on Computers and Communications (ISCC)

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“…The joint design of source and relay precoders has been extensively considered in the literature [7]- [14]. Different criteria, such as mutual information [7], [8] and mean square error (MSE) [11], were considered for the optimization of the source and relay precoders. For example, in [12], the authors proposed a unified framework to jointly optimize the source and relay Aissa Ikhlef and Robert Schober are with the Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada, E-mails: {aikhlef, rschober}@ece.ubc.ca.…”

Section: Introductionmentioning

confidence: 99%

Joint Source-Relay Optimization for Fixed Receivers in Multi-Antenna Multi-Relay Networks

Ikhlef

Schober

2014

IEEE Trans. Wireless Commun.

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Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-We jointly optimize the source and relay precoders for multi-antenna multi-relay networks employing a prefixed receiver. Prefixed receivers are of practical interest since they enable low complexity at the end-user's receiver as well as backward compatibility. To compute the source and relay precoders, we consider two different criteria. The objective of the first criterion is to maximize the worst stream signal-to-interference-plusnoise ratio (SINR) at the output of the receiver subject to source and relay transmit power constraints. Under the second criterion, we minimize the source and relay transmit powers subject to a certain quality-of-service constraint. Both optimization problems are non-convex. To solve them, we propose iterative alternating algorithms, where, in each iteration, we compute the precoders alternately, i.e., for each precoder optimization, we fix all the precoders except the one which is optimized. For both criteria, we formulate the optimization problem for the computation of the source precoder as a second order cone programming (SOCP) problem, for which the optimal solution can be found using interior point algorithms. For each relay precoder, we formulate the optimization problem as a semidefinite relaxation (SDR) problem for which ready-to-use solvers exist. If the solution to the SDR problem is not of rank one, matrix rankone decomposition or randomization is applied. We also provide sufficient conditions for the convergence of the proposed iterative alternating algorithms to a fixed point. Simulation results show that the performance of the proposed algorithms is close to the performance achieved if the source, relay, and receiver filters are jointly optimized.Index Terms-Cooperative relaying, multiple-input multipleoutput, joint optimization of source and relays, prefixed receivers.

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“…The work in (7; 8) investigates linear relay processing for the MIMO relay channel. In this paper, in contrast to (6)(7)(8)(9), we study an uncoded system, and we propose a nonlinear relaying scheme which is superior to linear relaying and performs close to the theoretical bound. Our proposed scheme is based on constellation permutation (10; 11) at the relay over different streams obtained by channel orthogonalization.…”

Section: Introductionmentioning

confidence: 99%