On the Feedback Reduction of Multiuser Relay Networks Using Compressive Sensing

Abstract

In this paper, we propose a feedback reduction scheme for full-duplex relay-aided multiuser networks. The proposed scheme permits the base station (BS) to obtain channel state information (CSI) from a subset of strong users under substantially reduced feedback overhead. More specifically, we cast the problem of user identification and CSI estimation as a block sparse signal recovery problem in compressive sensing (CS). Using existing CS block recovery algorithms, we first obtain the identity of the strong users and then estimate their CSI using the best linear unbiased estimator (BLUE). To minimize the effect of noise on the estimated CSI, we introduce a back-off strategy that optimally backsoff on the noisy estimated CSI and derive the error covariance matrix of the post-detection noise. In addition to this, we provide exact closed form expressions for the average maximum equivalent SNR at the destination user. Numerical results show that the proposed algorithm drastically reduces the feedback air-time and achieves a rate close to that obtained by scheduling schemes that require dedicated error-free feedback from all the network users.

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

“…Hence, we define the achievable throughput as the number of transmitted bits per unit time (bps/Hz). The network throughput is explicitly given by [26] …”

“…Note that when limited feedback is used, the amount of CSI that need to be collected becomes N p = rN t p , where r denotes the ratio of CSI that need to be collected and N t p denotes the total number of possible paths. To have more insight, we simulate the network throughput for τ = 1/600 [26].…”

“…In addition, a compressive sensing (CS)-based relay selection algorithm that reduces the feedback overhead of relay networks under the assumption of noisy feedback channels is presented in [25]. In [26], a full-duplex relay-aided multi-user network is considered and the proposed scheme permits the BS to obtain channel state information (CSI) from a subset of strong users. CS is used also as joint source-channel coding (JSCC) for the source and the relays in multi-relay compressive cooperative schemes [27].…”

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

“…We denote by Σ e,blue = E{e blue e * blue } the covariance matrix of e blue , then Σ e,blue = H * Σ −1 z H −1 . Note that the BLUE can also be used as a refinement tool after compressed sensing (CS) recovery as demonstrated in [8,9,17,18]. of the inverse moments of one sided correlated Gram matrix as follows…”

“…In particular, we provide closed-form expressions of the mean square error for the BLUE and for the LMMSE in both high and low SNR regimes. These expressions are quite useful for performance analysis in Compressed Sensing as it allows to evaluate the mean square error when the support of the estimated signal is known [8,9]. As a further application, we study the selection of the windowing factor used in exponentially weighted sample covariance matrices.…”