Outage Performance of a Two-branch Cooperative Energy-constrained Relaying Network with Selection Combining at Destination

Authors

DOI:

https://doi.org/10.4108/eai.27-6-2018.154833

Keywords:

Cooperative communication, Energy harvesting, Decode-and-forward, Power splitting, Selection combining

Abstract

In this paper, we investigate two-branch cooperative DF relaying networks with selection combining at the destination. Two intermediate relay-clusters (a conventional relay cluster and an energy-constrained relay cluster) are utilized to aid the communication between the source and the destination. We study two cases: direct link (DR) and no direct link (NDR) between the source and the destination. In each case, we consider two relay selection schemes: best sourceâ´Çrelay channel gain (BSR) and random relay selection (RAN). Thus, we have 4 protocols: DR-BSR, DR-RAN, NDR-BSR, and NDR-RAN. For the performance evaluation, we derive a closed-form expression for the outage probability of each of the four protocols. Our analysis is substantiated via a Monte Carlo simulation. As expected, the results show that the DR case outperforms the NDR case, and the BSR scheme outperforms the RAN scheme. The outage performances of the protocols are evaluated based on the system parameters, including the transmit power, the number of relays in each cluster, the energy harvesting eÿciency, the position of the two clusters, and the target rate. The outage performance of the system is improved when the transmit power increases, the energy harvesting eÿciency increases, the distance between the two clusters and the source and destination decreases, or the target rate decreases. We found good matches between the theoretical and Monte Carlo simulation results, verifying our mathematical analysis.

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Published

27-06-2018

How to Cite

Quang Nguyen, S. ., & Yun Kong, H. . (2018). Outage Performance of a Two-branch Cooperative Energy-constrained Relaying Network with Selection Combining at Destination. EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, 5(14), e2. https://doi.org/10.4108/eai.27-6-2018.154833