CrossNet: Joint Channel Estimation and Localization in Deep Learning Method

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-02-19 DOI:10.1109/LCOMM.2025.3543579

Chongyang Li;Tianqian Zhang;Shouyin Liu

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引用次数: 0

Abstract

This letter proposes CrossNet, a novel deep learning (DL) approach for joint channel estimation and outdoor localization. Similar to fingerprint methods that utilize features such as angle of arrival (AoA) and receive signal strength indicator (RSSI), CrossNet leverages neural networks to extract positional information from channel state information (CSI). However, instead of relying on direct matching within a database, CrossNet learns the implicit relationship between CSI and location through training, enabling more accurate and robust localization. The purpose of joint channel estimation and localization is to obtain more precise positioning information from more accurate channel estimation. We built a single-input single-output (SISO) downlink communication system on the DeepMIMO dataset and generated the necessary data for our experiments. We conducted multiple comparative experiments to evaluate the performance of CrossNet. Extensive comparative experiments demonstrated that CrossNet effectively utilizes pilots for user equipment (UE) localization and significantly improves localization accuracy through joint channel estimation.

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交叉网：深度学习方法中的联合信道估计和定位

这封信提出了CrossNet，一种新的深度学习（DL）方法，用于联合信道估计和室外定位。与利用到达角（AoA）和接收信号强度指示器（RSSI）等特征的指纹方法类似，CrossNet利用神经网络从通道状态信息（CSI）中提取位置信息。然而，CrossNet不依赖于数据库中的直接匹配，而是通过训练学习CSI和位置之间的隐含关系，从而实现更准确和稳健的定位。联合信道估计和定位的目的是在更精确的信道估计中获得更精确的定位信息。我们在DeepMIMO数据集上构建了一个单输入单输出（SISO）下行通信系统，并生成了实验所需的数据。我们进行了多个对比实验来评估CrossNet的性能。大量的对比实验表明，CrossNet有效地利用导频进行用户设备定位，并通过联合信道估计显著提高定位精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.