Opportunistic relay selection in multicast relay networks using compressive sensing

Abstract

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. However, for relay selection algorithms to make a selection decision, channel state information (CSI) from all cooperating relays is usually required at a central node. This requirement poses two important challenges. Firstly, CSI acquisition generates a great deal of feedback overhead (air-time) that could result in significant transmission delays. Secondly, the fed back channel information is usually corrupted by additive noise. This could lead to transmission outages if the central node selects the set of cooperating relays based on inaccurate feedback information. In this paper, we introduce a limited feedback relay selection algorithm for a multicast relay network. The proposed algorithm exploits the theory of compressive sensing to first obtain the identity of the "strong" relays with limited feedback. Following that, the CSI of the selected relays is estimated using linear minimum mean square error estimation. To minimize the effect of noise on the fed back CSI, we introduce a back-off strategy that optimally backs-off on the noisy estimated CSI. For a fixed group size, we provide closed form expressions for the scaling law of the maximum equivalent SNR for both Decode and Forward (DF) and Amplify and Forward (AF) cases. Numerical results show that the proposed algorithm drastically reduces the feedback air-time and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback channels.

Opportunistic Relay Selection With Limited Feedback

“…The analysis and mathematical derivations are different since the feedback criterion is different in both setups. In addition to this, the work in [18] considers an HD SRS with multiple destinations, while this work considers both SRS and MRS with a single destination. 2 The term back-off denotes an SNR reduction, applied by the source to the estimated SNRs, to reduce the likelihood of an SNR over-estimation error.…”

“…In HD mode, the source and the relay transmit on orthogonal channels, whereas in FD mode, the source and relay share a common channel and the relay transmits and receives simultaneously over the same channel. 1 During the course of this work, some preliminary results were presented in [18] for a relay-assisted multi-user broadcast setup. The analysis and mathematical derivations are different since the feedback criterion is different in both setups.…”

Distributed User Selection in Network MIMO Systems with Limited Feedback

“…However, this may result in a collision as stated before, and an outage may occur from the first iteration. Instead, we propose a Compressive Sensing based feedback algorithm, where we embrace collision and allow some users to feedback at the same time [6], [7]. Indeed, we set a threshold to sparsify users, i.e.…”

Analytical Derivation of the Inverse Moments of One-Sided Correlated Gram Matrices With Applications

“…Evaluating the LMMSE average estimation error is in general very difficult, and upper bounds on the mean square error are usually derived [19]. However, for the simple case where Σ x = σ 2 x I n , it is possible to obtain an approximation depending on the value of σ 2 x .…”