Opportunistic Relay Selection With Limited Feedback

Abstract

Relay selection is a simple technique that achieves spatial diversity in cooperative relay networks. Generally, relay selection algorithms require channel state information (CSI) feedback from all cooperating relays to make a selection decision. This requirement poses two important challenges, which are often neglected in the literature. Firstly, the fed back channel information is usually corrupted by additive noise. Secondly, CSI feedback generates a great deal of feedback overhead (air-time) that could result in significant performance hits. In this paper, we propose a compressive sensing (CS) based relay selection algorithm that reduces the feedback overhead of relay networks under the assumption of noisy feedback channels. The proposed algorithm exploits CS to first obtain the identity of a set of relays with favorable channel conditions. Following that, the CSI of the identified relays is estimated using least squares estimation without any additional feedback. Both single and multiple relay selection cases are considered. After deriving closed-form expressions for the asymptotic end-to-end SNR at the destination and the feedback load for different relaying protocols, we show that CS-based selection drastically reduces the feedback load and achieves a rate close to that obtained by selection algorithms with dedicated error-free feedback.

Relay Selection Based Full-Duplex Cooperative Systems Under Adaptive Transmission

“…Then, Zhong et al [7] analyzed the average capacity of opportunistic decode-and-forward (DF) RS, whereas the outage probability (OP) of FD RS in spectrum sharing networks was addressed in [8]. Likewise, Eltayeb et al [9] evaluated the performance of opportunistic RS with limited feedback, while investigations in the context of secure communications were recently reported in [10] and [11].…”

“…Furthermore, we consider a direct link with instantaneous and average SNRs γ S,D and γ S,D , respectively, and a PDF f γ S,D (γ ) = exp(−γ /γ S,D )/γ S,D . In the considered RS process, a single relay is selected among a set of relays, depending on which relay provides the best path between source and destination, i.e., the best S − R * k − D link, as also described in detail in [9]. To this effect and assuming maximum-ratio combining (MRC) at the destination, the output SNR is expressed as γ = γ S,D + γ R * k ,D , and its corresponding PDF is given by…”

Cognitive Incremental Relaying Networks with Spectrum Sharing and Hardware Impairments

“…The basic idea is that in addition to the direct transmission from the transmitter to the receiver, there are other cooperative nodes in the system, which can be used to enhance the diversity by relaying the source signal to the destination. In a multi-relay cooperative scenario, there are several different cooperation schemes studied in the literature based on the availability of channel side information (CSI) of the source-relay links and source-destination link, such as partial relay scheme (PR) [2] and opportunistic relay selection (OR) [3], [4]. Another promising technology for meeting the demands of the next generation of wireless networks is cognitive radio [5], which aims to provide a more efficient use of the radio spectrum.…”

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

“…In [24], a limited-feedback relay selection algorithm is also proposed and investigated for a multi-cast relay network. 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.…”