文献类型：会议论文

英文题名：Simultaneous Blind Demixing and Super-resolution via Vectorized Hankel Lift

作者：Wang, Haifeng[1];Chen, Jinchi[2];Fan, Hulei[1];Zhao, Yuxiang[3];Yu, Li[3]

机构：[1]China Mobile Zhejiang Res Innovat Inst, Hangzhou, Peoples R China;[2]East China Univ Sci & Technol, Sch Math, Shanghai, Peoples R China;[3]China Mobile Res Inst, Beijing, Peoples R China

会议论文集：59th Annual IEEE International Conference on Communications (IEEE ICC)

会议日期：JUN 09-13, 2024

会议地点：Denver, CO

语种：英文

外文关键词：Blind dimixing; blind super-resolution; vectorized Hankel lift

摘要：In this work, we investigate the problem of simultaneous blind demixing and super-resolution. Leveraging the subspace assumption regarding unknown point spread functions, this problem can be reformulated as a low-rank matrix demixing problem. We propose a convex recovery approach that utilizes the low-rank structure of each vectorized Hankel matrix associated with the target matrix. Our analysis reveals that for achieving exact recovery, the number of samples needs to satisfy the condition n greater than or similar to Ksr log(sn). Empirical evaluations demonstrate the recovery capabilities and the computational efficiency of the convex method.