Goal-Oriented CSI Feedback for MRT-Precoded Massive MIMO Communication Systems

Downlink channel state information (CSI) feedback typically results in an unacceptable overhead in frequencydivision-duplex (FDD) massive multiple-input multiple-output (MIMO) systems. To deal with this challenge, several deep learning (DL) based CSI compression and recovery approaches have been developed, which follow an auto-encoder architecture and aim at minimizing CSI reconstruction error. Different from the mainstream methodology mentioned above, in this letter, we follow a goal-oriented design philosophy. That is, instead of minimizing the reconstruction error, we train a deep neural network (NN) to compress the CSI such that the precoder using the compressed CSI as input can optimize the downlink transmission performance, i.e., minimize the bit error rate (BER) at the UEs. A two-stage training method is developed to train the NN. Experimental results demonstrate that the proposed scheme outperforms the existing solutions in terms of signal-tointerference-plus-noise ratio (SINR) and BER at terminal users