Cross-correlation-based signal pruning method (CCSPM) for effective signal distortion reduction in massive MIMO communications.

Massive Multiple-Input-Multiple-Output (MIMO) ensures spectral and energy improvements for a large number of communicating terminals in the base station. Signal distortion is observed due to overlapping channels that are adaptable to the receiving terminal's radiation patterns. For addressing this specific issue, a Cross Correlation-based Signal Pruning Method (CCSPM) is introduced. This method is introduced for suppressing signal distortion and reducing extraction complexity at the receiver terminal. The proposed method identifies the correlation using cumulative distortion rates between the adjacent channels during the beamforming process in massive MIMO. Such correlation is pruned until the channel is available between the base station and the receiver terminal. The process is validated using linear vector learning through which the signal vector representations are pursued. In the signal vector representation, the conventional signal is pruned for its maximum distortion rate post the correlation. The vector representation is recurrent until the least possible distortion rate is observed. If the signal is incomplete then the successive transmitting interval output is pruned to augment the previous ones. The proposed CCSPM improves the interval output by 9.39%, reduces distortion rate by 7.9%, and complexity by 9.09% for the varying frequency.