An energy efficient resource allocation and transmit antenna selection scheme in mm-wave using massive multiple-input multiple-output technology

The massive multiple-input multiple-output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and transmit antenna selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA and the TAS of wireless communication in millimeter-wave (mm-wave) with massive MIMO technology are considered. Two different solutions are developed for this research such as the deep learning method for efficient resource allocation process and optimization algorithm for TAS process. Here, the RA process is done with the help of attention-based capsule auto-encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, battle royale optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipment (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances spectral efficiency (SE), energy efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE, and SE.