Relay selection for DF-based cooperative vehicular systems

Abstract

In this paper, error performance analysis of decode-and-forward (DF)-based cooperative vehicular networks with relay selection is investigated. All of the links in the system are modeled as cascaded Nakagami-m distributed random variables which provide a realistic description of an intervehicular channel. We employ virtual-noise (VN)-based demodulation, maximum likelihood (ML) demodulation, cooperative maximal ratio combining (C-MRC), and log-likelihood-ratio-based transmissions in mitigating error propagation effect encountered in DF cooperative networks. In order to obtain more effective solutions and improve the system performance, we introduce hybrid anti-error propagation approaches, namely VN-MRC, VN-ML, and VN-LLR in which relay selection criterion is derived by using conditional bit error rate (BER) expressions of VN technique while data detection is performed by utilizing maximal ratio combining (MRC), ML detection, C-MRC and LLR transmission methods. The performance analyses and the numerical results for cascaded Nakagami-m channels have shown that VN-LLR approach provides significant performance improvement with respect to the other considered techniques in DF cooperative vehicular systems with relay selection.