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In massive multiple-input multiple-output (MIMO) systems, the large number ofantennas would bring a great challenge for the acquisition of the accuratechannel state information, especially in the frequency division duplex mode. Toovercome the bottleneck of the limited number of radio links in hybridbeamforming, we utilize the neural networks (NNs) to capture the inherentconnection between the uplink and downlink channel data sets and extrapolatethe downlink channels from a subset of the uplink channel state information. Westudy the antenna subset selection problem in order to achieve the best channelextrapolation and decrease the data size of NNs. The probabilistic samplingtheory is utilized to approximate the discrete antenna selection as acontinuous and differentiable function, which makes the back propagation of thedeep learning feasible. Then, we design the proper off-line training strategyto optimize both the antenna selection pattern and the extrapolation NNs.Finally, numerical results are presented to verify the effectiveness of ourproposed massive MIMO channel extrapolation algorithm.

Deep Learning Based Antenna Selection for Channel Extrapolation in FDD Massive MIMO