表題番号:2020C-191 日付:2021/03/29
研究課題直交多元接続との連携による非直交多元接続方式の特性改善に関する研究
研究者所属(当時) 資格 氏名
(代表者) 理工学術院 基幹理工学部 教授 前原 文明
研究成果概要

Sparse code multiple access (SCMA) is a promising approach for massive machine type communication (mMTC) which is one of the usage scenarios for the fifth-generation mobile communications systems (5G). SCMA improves the system capacity at relatively high CNR, but in low CNR, its non-orthogonality between users makes system capacity smaller than orthogonal frequency division multiple access (OFDMA). So far, we have proposed a hybrid approach using simultaneously SCMA and OFDMA to cope with this problem, and its effectiveness has been validated. In this work, we propose a system selection method employing deep learning based on user position information. In the proposed method, the multiple access scheme that maximizes the system throughput is selected from SCMA-only, OFDMA-only, and their hybrid approach. The effectiveness of the proposed scheme is demonstrated in terms of system throughput under different user distributions via computer simulations.