A novel massive MIMO strategy for optimal antenna selection via hybrid algorithm.

One of the capable technologies in networking systems is the Multiple-Input Multiple-Output (MIMO) technology as it provides better system capacity and a high Bit Error Rate (BER). Still, the transmit antenna selection remains as the greatest issue. More transmit antennas are necessary to achieve maximum capacity, which leads to increased power consumption. Therefore, for solving these issues in the M-MIMO system, this paper introduces a novel Wolf Adapted Herding algorithm (WA-HA) for selecting the optimal transmit antennas using multi-objective constraints that raise the capacity and Energy Efficiency (EE). The proposed algorithm combines the concept of Elephant Herding Optimization (EHO) and Grey Wolf Optimization (GWO) algorithm and it is referred to as the Wolf Adapted Herding Algorithm (WA-HA). This WA-HA algorithm optimises the number of transmit antennas and hence decides which antenna has to be chosen. Finally, the performance of the adopted method is evaluated over other traditional methods like MV-GSA, SLnO, I-SLnO, EHO and GWO in terms of various metrics like EE and capacity. In particular, the capacity of the proposed WA-HA method for ZF under set-up 1 is 51.02%, 30.61%, 22.44%, 8.163% and 55.10% better than the existing models. Furthermore, the computational time of our model is 10.96%, 26.13%, 14.08%, 13.48% and 10.26% superior to that of the existing models, respectively. [ABSTRACT FROM AUTHOR]