Online Publication Date2021-03-09
Print Publication Date2021-04
Permanent link to this recordhttp://hdl.handle.net/10754/668029
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AbstractCombined relay selection only requires two relays to forward signals transmitted on multiple subcarriers, but the optimal outage performance is almost surely achievable in the high signal-to-noise ratio (SNR) region. However, because combined relay selection involves the generation of the full set of two-relay combinations, the selection complexity of combined relay selection is much higher than that of per-subcarrier relay selection when the number of relays goes large. This drawback restricts the implementation of combined relay selection in dense networks. To overcome this drawback, we propose to enable combined relay selection by supervised machine learning (ML). Because the training procedure is off-line, the proposed implementation scheme can considerably reduce the selection complexity and the processing latency. We carry out extensive experiments on TensorFlow 2.1 over a graphics processing unit (GPU) aided computing cloud server to validate the effectiveness of the proposed scheme. The experimental results confirm that supervised ML can provide near-optimal performance with lower computing latency that well matches that provided by brute-force search and the optimal relay selection in a per-subcarrier manner.
CitationDang, S., Tang, J., Li, J., Wen, M., Abdullah, S., & Li, C. (2021). Combined Relay Selection Enabled by Supervised Machine Learning. IEEE Transactions on Vehicular Technology, 1–1. doi:10.1109/tvt.2021.3065074
SponsorsThis work was supported in part by National Natural Science Foundation of China under Grant 61872102, in part by Guangxi Natural Science Foundation under Grant AD19245043, in part by Nanning Excellent Young Scientist Program under Grant RC20190201, in part by Guangxi Beibu Gulf Economic Zone Major Talent Program, in part by the International Collaborative Research Program of Guangdong Science and Technology Department under Grant No.2020A0505100061, in part by the Pearl River Nova Program of Guangzhou under Grant 201806010171, and in part by the Fundamental Research Funds for the Central Universities under Grant 2019SJ02.