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To fully exploit the advantages of massive multiple-input multiple-output(m-MIMO), accurate channel state information (CSI) is required at thetransmitter. However, excessive CSI feedback for large antenna arrays isinefficient and thus undesirable in practical applications. By exploiting theinherent correlation characteristics of complex-valued channel responses in theangular-delay domain, we propose a novel neural network (NN) architecture,namely ENet, for CSI compression and feedback in m-MIMO. Even if the ENetprocesses the real and imaginary parts of the CSI values separately, itsspecial structure enables the network trained for the real part only to bereused for the imaginary part. The proposed ENet shows enhanced performancewith the network size reduced by nearly an order of magnitude compared to theexisting NN-based solutions. Experimental results verify the effectiveness ofthe proposed ENet.

A Lightweight Deep Network for Efficient CSI Feedback in Massive MIMO Systems