Dual-Polarized FDD Massive MIMO: A Comprehensive Framework

IEEE Transactions on Wireless Communications(2022)

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摘要
We propose a comprehensive scheme for realizing a massive multiple-input multiple-output (MIMO) system with dual-polarized antennas in frequency division duplexing (FDD) mode. Dual-polarized arrays are commonly employed due to the favorable property that, in principle, they can double the number of channel spatial degrees of freedom with a less-than-proportional increase in array size. However, processing a dual-polarized massive MIMO channel is demanding due to the high channel dimension and the lack of Uplink-Downlink (UL-DL) channel reciprocity in FDD mode. In particular, the difficulty arises in common channel training and DL precoding in a multi-user setup. To address this, we develop a unified framework consisting of three steps: (1) covariance estimation to efficiently estimate the UL covariance from noisy UL pilots; (2) a UL-DL covariance transformation method that obtains the DL covariance from the estimated UL covariance, eliminating the need for DL channel covariance training via pilot transmission; (3) a joint multi-user DL channel training method, which enables the BS to estimate effective DL channels given any protocol-specific pilot dimension and to use them for interference-free DL beamforming and data transmission. Through extensive simulations, we show that our scheme is applicable to a variety of communication scenarios in terms of the number of antennas, UL and DL pilot dimensions, and angular scattering properties. Unlike the common trend in the literature, we do not make strong structural assumptions about the wireless channel (such as angular sparsity), ensuring a general treatment of the problem.
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关键词
Active channel sparsification,channel covariance estimation,dual-polarized FDD massive MIMO,multi-user channel training,uplink-downlink covariance transformation
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