| 摘要: |
| 超大规模多输入多输出(Extremely Large-scale Multiple-Input Multiple-Output,XL-MIMO)作为6G的关键候选技术之一，通过配置数百乃至数千根天线单元，极大地提高了无线通信和传感的频谱效率。为了充分发挥超大规模MIMO技术的潜力，获取准确的信道状态信息至关重要，这使得信道估计成为该领域的一个核心议题。概述了超大规模MIMO信道的近场及非平稳特性，综述了近年来针对此类场景发展的信道估计方法，指出现有研究往往基于较为简化的假设或未能充分考虑超大规模MIMO特有的信道属性，导致在实际应用中的准确性不足。最后总结分析了当前超大规模MIMO信道估计方法的挑战及潜在的解决思路。 |
| 关键词: 超大规模多输入多输出(XL-MIMO) 信道估计 压缩感知 深度学习 参数估计 |
| DOI：10.20079/j.issn.1001-893x.240722002 |
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| 基金项目:国家自然科学基金资助项目(61701062);中国博士后科学基金(2019M651649);江苏省博士后科研基金(2018K041c);重庆市教育委员会科学技术研究项目(KJQN202100649,KJQN202000612) |
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| Extremely Large-scale MIMO Channel Estimation for 6G Networks:Progress and Challenges |
| PU Xumin,ZHANG Qingyu,WANG Kehao,LIU Yinlan |
| (School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China) |
| Abstract: |
| Extremely large-scale multiple-input multiple-output(XL-MIMO),as one of the key candidate technologies for future 6G,dramatically improves the spectral efficiency of wireless communication and sensing by equipping hundreds or even thousands of antenna array elements.To realize the full potential of XL-MIMO techniques,accurate channel state information(CSI) is essential,which makes channel estimation a central topic in the field.The authors describe the near-field property and spatial non-stationarity of XL-MIMO,review channel estimation methods developed for such scenarios in recent years,and point out the fact that existing studies are often based on simplified assumptions or fail to fully consider the specific channel properties of XL-MIMO,resulting in insufficient accuracy in practical applications.Finally,they summarize and analyze the challenges and potential solutions for current XL-MIMO channel estimation methods. |
| Key words: extremely large-scale multiple-input multiple-output(XL-MIMO) channel estimation compressed sensing deep learning parameter estimation |
