计算机科学
多输入多输出
同步(交流)
二进制数
偏移量(计算机科学)
能量(信号处理)
统计的
算法
载波频率偏移
探测器
频道(广播)
实时计算
人工智能
频率偏移
电信
数学
统计
正交频分复用
算术
程序设计语言
作者
Meiyu Wang,Die Hu,Lianghua He,Wu Jun
出处
期刊:IEEE Wireless Communications Letters
[Institute of Electrical and Electronics Engineers]
日期:2022-05-01
卷期号:11 (5): 1067-1071
被引量:6
标识
DOI:10.1109/lwc.2022.3156395
摘要
This letter proposes a deep-learning-based initial access (IA) method for a millimeter-wave (mmW) multiple-input multiple-output (MIMO) system. By detecting the primary synchronization signal (PSS) in the IA block, UE can discover and synchronize with the BS. PSS detection can be modeled as a binary hypothesis testing problem with unkonwn channel, carrier frequency offset (CFO), and timing offset (TO). Unlike the conventional methods using energy as the detection statistic, the proposed method uses probability as the detection statistic. Specifically, we first preprocess the received signal, and then input the results into a pre-trained convolutional neural network (CNN). The CNN can output the probability that PSS is present. After comparing the maximum probability with the threshold, the UE can determine whether there is PSS. Once PSS is detected, the estimation of TO can be obtained at the same time. Simulation results demonstrate that, the proposed IA method can outperform the conventional energy-based IA method in both miss detection rate performance of PSS and normalized mean square error (NMSE) performance of TO.
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