Massive MIMO with Advanced Multiuser Detector with Improved Shark Smell Optimization

Massive MIMO systems employ hundreds of antennas to service a number of wireless broadband terminals at the same time. Massive MIMO systems need for simplified, maybe linear techniques for efficient beamforming as well as decoding. To address the signaling overhead complexity, effective signal processing methods must be applied on both ends. When massive MIMO networks could be successfully deployed, there are several fundamental problems that must be resolved. This paper intends to introduce an advanced model for detecting the Gaussian channel matrix. Here, the major objective is to attain minimal Bit Error Rate (BER) between the transmitted and received bits, for which an optimization concept is integrated. Accordingly, the Gaussian channel matrix is optimally tuned, such that the BER among transmitted and received bits should be minimal. For optimization purpose, an Opposition Learning Insisted shark smell optimization (OLISSO) is implemented in this work. Finally, the outcomes of the presented scheme are evaluated over the other extant schemes with respect to varied measures. Moreover, error analysis was performed with respect to MAE, MSE and RMSE, respectively.