Deep Learning-Based Joint Detection for OFDM-NOMA Scheme

被引：33

作者：

Xie, Yihang ^{[1
]}

Teh, Kah Chan ^{[2
]}

Kot, Alex C. ^{[2
]}

机构：

[1] Nanyang Technol Univ, Ctr Informat Sci Syst, Sch Elect & Elect Engn, Singapore 639798, Singapore

[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

IEEE COMMUNICATIONS LETTERS | 2021年 / 25卷 / 08期

关键词：

NOMA; Feature extraction; Deep learning; 5G mobile communication; Fading channels; Wireless communication; Signal detection; deep learning; 5G; multi-path fading channel; signal detection; NONORTHOGONAL MULTIPLE-ACCESS; POWER;

D O I：

10.1109/LCOMM.2021.3077878

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

Non-orthogonal multiple access (NOMA) technique has drawn much attention in recent years. It has also been a promising technique for the fifth-generation (5G) wireless communication system and beyond. In this letter, we develop a novel deep learning (DL) aided receiver for NOMA joint signal detection. The DL-based receiver serves as an end-to-end mode, which simultaneously fulfills the function of channel estimation, equalization, and demodulation. Compared with the traditional signal detection method for the NOMA scheme, the proposed deep learning method shows feasible improvement in performance and robustness with the tapped-delay line (TDL) channel model, which is adopted for the 5G communication environment.

引用

页码：2609 / 2613

页数：5

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