2020最佳AI新基建年度榜
您正在使用IE低版浏览器,为了您的雷锋网账号安全和更好的产品体验,强烈建议使用更快更安全的浏览器
此为临时链接,仅用于文章预览,将在时失效
业界 正文
发私信给木子
发送

0

基于实测信道的AI赋能无线通信:信道反馈

本文作者:木子 2020-09-25 10:27 专题:2020年全国人工智能大赛
导语:为了探索AI在通信系统中的实际性能,鹏城实验室等单位组织了全国人工智能大赛(NAIC)“AI+无线通信”赛道​,初赛赛题为“基于AI的无线通信信道的压缩及恢复”

基于实测信道的AI赋能无线通信:信道反馈

作者:Jiajia Guo1, Xiangyi Li1, Muhan Chen1, Peiwen Jiang1, Tingting Yang2, Weiming Duan2,Haowen Wang3, Shi Jin1, Quan Yu2

单位:1. National Mobile Communications Research Laboratory, Southeast University; 2. Peng Cheng Laboratory; 3. Laboratory of Broadband Wireless Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences

近年来,人工智能(Artificial Intelligence,AI)在信号处理、信道估计、编码设计等通信领域取得了重大突破,打破了传统通信系统的设计瓶颈,作为一项突破性技术使得智能通信成为未来通信系统研究的热门方向之一。基于深度学习(Deep Learning,DL)的信道状态信息(Channel State Information,CSI)反馈技术因其突出的性能优势得到了广泛关注,但目前相关研究仅使用模拟生成的数据集来训练和测试,无法保证AI算法在实际通信系统中仍然具有良好的性能。

为了探索AI在通信系统中的实际性能,鹏城实验室等单位组织了全国人工智能大赛(NAIC)“AI+无线通信”赛道,初赛赛题为“基于AI的无线通信信道的压缩及恢复”。本文详细描述了该比赛的信道数据采集过程,同时为该比赛提供了一个基于DL的CSI反馈参考架构:QuanCsiNet,实现了真实信道场景采集的高维信道数据的压缩、量化、反馈和重建,为AI在未来通信系统中的实际部署和使用奠定了基础。

真实信道场景为图1所示的办公室场景,发射机在图中所示位置固定,接收机沿红点轨迹运动。对测量得到的真实信道数据进行图2所示的预处理,得到最终使用的数据集。

基于实测信道的AI赋能无线通信:信道反馈

图1 实测信道数据场景示意图

基于实测信道的AI赋能无线通信:信道反馈

图2 实测信道数据处理流程

QuanCsiNet的网络结构如下图所示。编码器用于对CSI进行特征提取和压缩,量化模块用于将压缩测量值用有限位表示,转化为比特流便于实际系统存储和传输;逆量化模块用于将比特流恢复成压缩测量值,译码器用于特征解压缩和信道恢复。

基于实测信道的AI赋能无线通信:信道反馈

图3 QuanCsiNet网络结构

具体来说,文章

  • 首次针对实测信道数据进行处理,提出了一种真实信道场景下的CSI反馈架构,为后续研究提供了可扩展的参考设计。

  • 引入了量化和逆量化模块,将反馈测量值转化为比特流,符合实际系统存储传输要求。

  • 评估了基于DL的CSI反馈方案在实际信道环境中的性能,推动后续研究和实际部署。


整体而言,本文对真实信道场景下采集的信道数据进行处理,设计了一种以比特流形式进行反馈的基于DL的CSI反馈架构,并衡量了其重建性能与复杂度,以期更多研究者为智能通信的实际应用作出贡献。


论文下载链接:(请戳此处

引用格式:Jiajia Guo, Xiangyi Li, Muhan Chen, Peiwen Jiang, Tingting Yang, Weiming Duan, Haowen Wang, Shi Jin, Quan Yu, “AI Enabled Wireless Communications with Real Channel Measurements: Channel Feedback”, Journal of Communications and Information Networks, vol. 5, no. 3, pp. 310-317, Sep. 2020.


本文由论文作者供稿。

欢迎课题组投递成果宣传稿(可在文章下方留言联系我们)!


作者简介

基于实测信道的AI赋能无线通信:信道反馈

Jiajia Guo(郭佳佳)received the B.S. degree from Nanjing University of Science and Technology, Nanjing, China, in 2016, and the M.S. degree from University of Science and Technology of China, Hefei, China, in 2019. He is currently working towards the Ph.D. degree in information and communications engineering, Southeast University, China. His research interests currently include, deep learning, neural network compression, massive MIMO, and machine learning in communications.

基于实测信道的AI赋能无线通信:信道反馈

Xiangyi Li(李湘宜)received the B.S. degree from School of Mathematics, Tianjin University, Tianjin, China, in 2017, and the M.S. degree from Centre for Applied Mathematics, Tianjin University, in 2020. She is currently working toward the Ph.D. degree in information and communications engineering, Southeast University, China. Her main research focuses on deep learning application in wireless communication and massive MIMO systems.

基于实测信道的AI赋能无线通信:信道反馈

Muhan Chen(陈慕涵)received the B.S. degree from the School of Information Science and Engineering, Southeast University, Nanjing, China, in 2019. She is currently working toward the M.S. degree with the School of Information Science and Engineering, Southeast University, Nanjing, China. Her research interests center around deep learning applications in wireless communication systems.

基于实测信道的AI赋能无线通信:信道反馈

Peiwen Jiang(姜培文)received the B.S. degree from Southeast University, Nanjing, China in 2019. He is currently working toward the Ph.D. degree with the School of Information Science and Engineering, Southeast University. His research interests include deep learning-based channel estimation and signal detection in communications.

基于实测信道的AI赋能无线通信:信道反馈

Tingting Yang(杨婷婷)received her B.Sc. and Ph.D. degrees from Dalian Maritime University, China, in 2004 and 2010, respectively. She is currently a Research Professor at Peng Cheng Laboratory, China. Her research interests are in the areas of maritime wideband communication networks, AI-empowered wireless communications. She serves as the Associate Editor-in-Chief of the IET Communications, as well as the Advisory Editor for SpringerPlus.

基于实测信道的AI赋能无线通信:信道反馈

Weiming Duan(段为明)is now a Senior Engineer in Peng Cheng Laboratory. He received his M.S. degree in communication and information system from the University of Electronic Science and Technology of China (UESTC) in 1999. In the same year, he joined Huawei Wireless Research Department in Shanghai and has worked there for 20 years. He has worked on baseband algorithm for 3G/4G, advanced receiver for 4G, waveform concept research for 5G, and has also been deeply involved in low-level algorithm library optimization to speed up largescale system simulation.

基于实测信道的AI赋能无线通信:信道反馈

Haowen Wang(王浩文)is a Senior Engineer of Laboratory of Broadband Wireless Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences (SIMIT). He received his B.S. and M.S. degrees from EE department and College of Software of Fudan University. In SIMIT, Haowen is a leader of wireless technology R&D group. He has many years of experience in the test and verification for the new technologies of wireless communications. His job and research interests include RF data acquisition, channel measurement, verification and test solution.

基于实测信道的AI赋能无线通信:信道反馈

Shi Jin(金石)[corresponding author] received his B.S. degree in communications engineering from Guilin University of Electronic Technology, Guilin, China, in 1996, his M.S. degree from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2003, and his Ph.D. degree in information and communications engineering from Southeast University, Nanjing, in 2007. From June 2007 to October 2009, he was a Research Fellow with the Adastral Park Research Campus, University College London, London, U.K. He is currently with the Faculty of the National Mobile Communications Research Laboratory, Southeast University. His research interests include space time wireless communications, random matrix theory, and information theory. He serves as an Associate Editor for the IEEE Transactions on Wireless Communications, IEEE Communications Letters, and IET Communications. He and his coauthors have been awarded the 2011 IEEE Communications Society Stephen O. Rice Prize Paper Award in the field of communication theory and the 2010 Young Author Best Paper Award by IEEE Signal Processing Society.

基于实测信道的AI赋能无线通信:信道反馈

Quan Yu(于全)received his B.S. degree in radio physics from Nanjing University, China, in 1986, his M.S. degree in radio wave propagation from Xidian University, China, in 1988, and his Ph.D. degree in fiber optics from the University of Limoges, France, in 1992. He is currently a Research Professor at Peng Cheng Laboratory. His main areas of research interest are the architecture of wireless networks and cognitive radio. He is an Academician of the Chinese Academy of Engineering (CAE) and the founding Editor-in-Chief of the Journal of Communications and Information Networks (JCIN).

雷锋网雷锋网(公众号:雷锋网)

雷锋网版权文章,未经授权禁止转载。详情见转载须知

基于实测信道的AI赋能无线通信:信道反馈

分享:
相关文章

文章点评:

表情
最新文章
请填写申请人资料
姓名
电话
邮箱
微信号
作品链接
个人简介
为了您的账户安全,请验证邮箱
您的邮箱还未验证,完成可获20积分哟!
请验证您的邮箱
立即验证
完善账号信息
您的账号已经绑定,现在您可以设置密码以方便用邮箱登录
立即设置 以后再说