Abstract
Abstract-Due to the huge unlicensed spectrum, millimeter wave (MMW) communication are gaining much attention for 5G cellular systems. To combat the severe path loss encountered, MMW communication often resorts to multiple-input multipleoutput (MIMO) beamforming. The capacity-optimal transmit and receive beamforming vectors are determined by the singular value decomposition (SVD) of channel matrix. Therefore, the conventional way to realize SVD beamforming requires channel estimation, which poses a formidable challenge for MMW systems with large antenna arrays and a limited number of radio frequency (RF) chains. To bypass the burdensome channel estimation, this paper proposes an iterative compressed training scheme to realize SVD beamforming in MMW systems. Relying on the channel reciprocity in time division duplex (TDD), the proposed scheme employs power iteration to gradually approach the optimal beamforming vectors, which only needs to estimate several channel-related vectors instead of the channel matrix. Furthermore, by exploiting the spatial sparsity in MMW channels, the propose scheme uses compressive sensing to estimate the involved channel-related vectors. This processing not only allows for the limited number of RF chains, but also greatly alleviates the training burden. Simulations show that the proposed scheme can approach the perfect performance limits with quite moderate training overhead.
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