Neural Network-Based Multi-DOA Tracking for High Speed Railway Communication Systems

被引：3

作者：

Zheng, Yu ^{[1
]}

Xiao, Yue ^{[1
]}

Ma, Zheng ^{[1
]}

Diamantoulakis, Panagiotis D. ^{[1
,2
]}

Karagiannidis, George K. ^{[1
,2
]}

机构：

[1] Southwest Jiaotong Univ, Prov Key Lab Informat Coding & Transmiss, Chengdu 611756, Peoples R China

[2] Aristotle Univ Thessaloniki, Elect & Comp Engn Dept, Thessaloniki 54124, Greece

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2022年 / 71卷 / 10期

关键词：

Direction-of-arrival estimation; Neural networks; Rail transportation; Interference; Estimation; Training; Approximation algorithms; High-speed railway (HSR); DOAs (direction-of-arrivals) tracking; radial-basis function neural network (RBFNN); OF-ARRIVAL ESTIMATION; ANTENNA; VIADUCT;

D O I：

10.1109/TVT.2022.3188848

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Along with the rapid development of high-speed railway (HSR), it is of vital importance to explore new technologies to meet the stringent requirements on wireless transmission latency and reliability in such high mobility scenarios. To address this, fast beamforming schemes with precise direction-of-arrival (DOA) can achieve effective channel gain and improve interference mitigation. In this paper, we propose a novel radial-basis function neural network (RBFNN)-based method for the multi-DOA tracking, in which the RBFNN is trained by the location-based prior information. Consequently, the DOA tracking problem is transformed into an RBFNN with trained input/output pairs. The effectiveness of the proposed scheme in terms of complexity and accuracy is verified by simulation results. Finally, the impact of the shapes of the array on estimation accuracy is also discussed.

引用

页码：11284 / 11288

页数：5

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