Development of hybrid sewing training-based dingo optimization algorithm for massive MIMO spectral efficiency maximization system

A Base Station (BS) with several antenna elements serves a number of users in the frequency of the same time- slot in a huge multiuser "Multiple-Input Multiple-Output (MIMO)". It has emerged as a viable method for the future generation of wireless networks due to its capacity to enhance both spectrum and efficiency of energy, even with straightforward linear processing like "Maximum Ratio Combining (MRC), Linear Minimum Mean Square Error (LMMSE) and Zero Forcing (ZF)". In a power allocation scheduling method, only users who do not interfere with one another can change their power at any one time. These studies all take into account orthogonal channels. However, the "Spectral Efficiency (SE)" is reduced by the channels' non-orthogonality. This encourages us to research how to maximize spectral efficiency in "Massive MIMO (M-MIMO)" systems by allocating power for both uplink and downlink transmission. Here, the Hybrid of Sewing Training and Dingo Optimization (HSTDO) is developed for executing the best resource allocation in the M-MIMO system to improve SE performance which helps to enhance the SE of the M-MIMO system. The simulation is carried out to compare the developed model with the conventional works to reveal the better spectral efficiency of the suggested algorithm.