Bayesian Neural Network Detector for an Orthogonal Time Frequency Space Modulation

被引：3
|
作者：
Kosasih, Alva [1 ]
Qu, Xinwei [1 ]
Hardjawana, Wibowo [1 ]
Yue, Chentao [1 ]
Vucetic, Branka [1 ]
机构：
[1] Univ Sydney, Ctr Excellence Telecommun, Sydney, NSW 2006, Australia
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2022年 / 11卷 / 12期
基金：
澳大利亚研究理事会;
关键词：
OTFS; neural network; interference cancellation; detection; mobile cellular networks;
D O I：
10.1109/LWC.2022.3209076
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The orthogonal time frequency space (OTFS) modulation is proposed for beyond 5G wireless systems to deal with high mobility communications. The existing low complexity OTFS detectors' performance is suboptimal in rich scattering environments where there are a large number of moving reflectors that reflect the transmitted signal towards the receiver. In this letter, we propose an OTFS detector, referred to as the BPICNet OTFS detector that integrates NN, Bayesian inference, and parallel interference cancellation concepts. Simulation results show that the proposed detector outperforms the state-of-the-art.
引用
收藏
页码：2570 / 2574
页数：5
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