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Near-Field Sparse Channel Representation and Estimation in 6G Wireless Communications

Xing ZhangHaiyang ZhangYonina C. Eldar
Dec 2022
The employment of extremely large antenna arrays and high-frequency signalingmakes future 6G wireless communications likely to operate in the near-fieldregion. In this case, the spherical wave assumption which takes into accountboth the user angle and distance is more accurate than the conventional planarone that is only related to the user angle. Therefore, the conventional planarwave based far-field channel model as well as its associated estimationalgorithms needs to be reconsidered. Here we first propose adistance-parameterized angular-domain sparse model to represent the near-fieldchannel. In this model, the user distance is included in the dictionary as anunknown parameter, so that the number of dictionary columns depends only on theangular space division. This is different from the existing polar-domainnear-field channel model where the dictionary is constructed on anangle-distance two-dimensional (2D) space. Next, based on this model, jointdictionary learning and sparse recovery based channel estimation methods areproposed for both line of sight (LoS) and multi-path settings. To furtherdemonstrate the effectiveness of the suggested algorithms, recovery conditionsand computational complexity are studied. Our analysis shows that with thedecrease of distance estimation error in the dictionary, the angular-domainsparse vector can be exactly recovered after a few iterations. The high storageburden and dictionary coherence issues that arise in the polar-domain 2Drepresentation are well addressed. Finally, simulations in multi-usercommunication scenarios support the superiority of the proposed near-fieldchannel sparse representation and estimation over the existing polar-domainmethod in channel estimation error.