Transformer-Based Detector for OFDM With Index Modulation

被引：8

作者：

Zhang, Dexin ^{[1
]}

Wang, Sixian ^{[1
]}

Niu, Kai ^{[1
]}

Dai, Jincheng ^{[1
]}

Wang, Sen ^{[2
]}

Yuan, Yifei ^{[2
]}

机构：

[1] Beijing Univ Posts & Telecommun BUPT, Key Lab Universal Wireless Commun, Minist Educ, Beijing 100876, Peoples R China

[2] China Mobile Res Inst CMRI, Beijing 100053, Peoples R China

来源：

IEEE COMMUNICATIONS LETTERS | 2022年 / 26卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Detectors; Transformers; Indexes; Neural networks; OFDM; Modulation; Feature extraction; Index modulation (IM) detector; deep learning (DL); transformer; self-attention mechanism; LEARNING-BASED DETECTOR;

D O I：

10.1109/LCOMM.2022.3158734

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

A deep learning (DL)-based detector utilizing the Transformer framework is proposed for orthogonal frequency-division multiplexing with index modulation (OFDM-IM) systems, termed as TransIM. Concretely, TransIM adopts a two-step detection method. First, the neural networks with the Transformer block as the core provide soft probabilities of different transmitted symbols. Then, conventional signal detection methods are performed based on those probabilities to make final decisions. This method is verified to improve system error performance significantly, albeit at the cost of slightly increased complexity. Simulation results indicate that the proposed TransIM detector fares better than existing DL-based ones regarding bit error rate (BER) performance.

引用

页码：1313 / 1317

页数：5

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