无线电通信技术2025，Vol.51Issue(1)：20-28,9.DOI:10.3969/j.issn.1003-3114.2025.01.003
面向数据丢失的极大规模天线阵列近场信道估计方法
Near-field Channel Estimation for Extremely Large-scale Antenna Arrays with Data Loss
摘要
Abstract
6G technology chooses higher frequency bands and larger-scale antenna arrays to enhance communication speed,resolution,system capacity,and other metrics.The application of these technologies expands the near-field radiation range.The proliferation of antenna elements in extremely large-scale arrays brings large amount of data to be processed,which improves the channel estimation complexity.Mo-reover,extremely large-scale arrays are more vulnerable to data loss.Therefore,low-complexity channel estimation with data loss becomes an urgent problem to be solved.To address this issue,the sparsity of the near-field channel model is discussed in the fractional Fourier transform domain,and then a low-complexity channel estimation method based on the Compressed Sampling Matching Pursuit(CoSaMP)algorithm is proposed.The performance of the proposed algorithm is analyzed,for which the Cramér-Rao Bound(CRB)is provided.Finally,different factors,Signal-to-Noise Ratio(SNR),loss rate,and sparsity,on the performance of the proposed are presented and discussed using simula-tions.Performance on other popular compressing based methods,such as the Orthogonal Matching Pursuit(OMP),are presented,which veri-fies the correctness and effectiveness of the proposed method.Our proposed method takes into account the factor of data loss,which paves the way for theoretical and practical development of future wireless communication systems.关键词
极大规模天线阵列/数据丢失/信道估计/分数傅里叶变换/压缩采样匹配追踪Key words
extremely large-scale arrays/data loss/channel estimation/fractional Fourier transform/CoSaMP分类
信息技术与安全科学引用本文复制引用
王韵唐,袁行方,时艺轩,汤宇欣,苗红霞,彭木根..面向数据丢失的极大规模天线阵列近场信道估计方法[J].无线电通信技术,2025,51(1):20-28,9.基金项目
国家重点研发计划(2021YFB2900200) （2021YFB2900200）
国家自然科学基金(62201078) （62201078）
北京市自然科学基金(L242090) National Key R&D Program of China(2021YFB2900200) （L242090）
National Natural Science Foundation of China(62201078) （62201078）
Beijing Mu-nicipal Natural Science Foundation of China(L242090) （L242090）
