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Tensor-based Multi-dimensional Wideband Channel Estimation for mmWave Hybrid Cylindrical Arrays

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Document pages: 15 pages

Abstract: Channel estimation is challenging for hybrid millimeter wave (mmWave)large-scale antenna arrays which are promising in 5G B5G applications. Thechallenges are associated with angular resolution losses resulting from hybridfront-ends, beam squinting, and susceptibility to the receiver noises. Based ontensor signal processing, this paper presents a novel multi-dimensionalapproach to channel parameter estimation with large-scale mmWave hybrid uniformcircular cylindrical arrays (UCyAs) which are compact in size and immune tomutual coupling but known to suffer from infinite-dimensional array responsesand intractability. We design a new resolution-preserving hybrid beamformer anda low-complexity beam squinting suppression method, and reveal the existence ofshift-invariance relations in the tensor models of received array signals atthe UCyA. Exploiting these relations, we propose a new tensor-based subspaceestimation algorithm to suppress the receiver noises in all dimensions (time,frequency, and space). The algorithm can accurately estimate the channelparameters from both coherent and incoherent signals. Corroborated by theCramér-Rao lower bound (CRLB), simulation results show that the proposedalgorithm is able to achieve substantially higher estimation accuracy thanexisting matrix-based techniques, with a comparable computational complexity.

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