Deep Learning Based Hybrid Multiple Access Consisting of SCMA and OFDMA Using User Position Information
Deep Learning Based Hybrid Multiple Access Consisting of SCMA and OFDMA Using User Position Information
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DOI：
10.1109/icaiic51459.2021.9415180
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Yuta Kumagai;Naoya Gonda;Yukiko Shimbo;Hirofumi Suganuma;F. Maehara
中科院分区：
文献类型：
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作者：
Yuta Kumagai;Naoya Gonda;Yukiko Shimbo;Hirofumi Suganuma;F. Maehara
本文提出了一种基于深度学习的上行链路混合多址接入方案，该方案由稀疏码多址接入（SCMA）和正交频分多址接入（OFDMA）组成。SCMA在载波噪声比（carrier-to-noise ratio，CNR）较高时提高了系统吞吐量。然而，当CNR低时，与OFDMA相比，SCMA性能显著降低。为了克服这个问题，所提出的方案引入了SCMA和OFDMA的组合作为一种新的多址模式。该方案通过深度学习利用用户位置信息，在仅CDMA、仅OFDMA或其组合之间确定适当的模式。通过计算机仿真，在不同用户分布下的系统吞吐量证明了所提出的方案的有效性。
This paper proposes a deep-learning-based uplink hybrid multiple access scheme consisting of both sparse code multiple access (SCMA) and orthogonal frequency-division multiple access (OFDMA). SCMA improves the system throughput when the carrier-to-noise ratio (CNR) is high. However, SCMA performance is significantly degraded, compared to OFDMA, when the CNR is low. To overcome this problem, the proposed scheme introduces a combination of SCMA and OFDMA as a novel multiple access pattern. The scheme determines the appropriate pattern among SCMA-only, OFDMA-only, or their combination, by utilizing user position information through deep learning. The effectiveness of the proposed scheme is demonstrated in terms of system throughput under different user distributions via computer simulations.