MIMO technology has become very popular in a wireless communication system because of the many advantages of multiple antennas at the transmitting end and receiving end. The main advantages of MIMO systems are higher data rate and higher reliability without the need of extra power and bandwidth. The MIMO system provides higher data rate by using spatial multiplexing technique and higher reliability by using diversity technique. The MIMO systems have not only advantages, but also have disadvantages. The main disadvantage of MIMO system is that the multiple antennas required extra high cost RF modules. The extra RF modules increase the cost of wireless communication systems. In this research, the antenna selection techniques are proposed to minimize the cost of MIMO systems. Furthermore, this research also presents techniques for antenna selection to enhance the capacity of channel in MIMO systems.

With the advent of massive MIMO and mmWave, Antenna selection is the new frontier in hybrid beamforming employed in 5G base stations. Tele-operators are reworking on the components while upgrading to 5G where the antenna is a last-mile device. The burden on the physical layer not only demands smart and adaptive antennas but also an intelligent antenna selection mechanism to reduce power consumption and improve system capacity while degrading the hardware cost and complexity. This work focuses on reducing the power consumption and finding the optimal number of RF chains for a given millimeter wave massive MIMO system. At first, we investigate the power scaling method for both perfect Channel State Information (CSI) and imperfect CSI where the power is reduced by ��/���� and ��/√���� respectively. We further propose to reduce the power consumption by emphasizing on the subdued resolution of Analog-to-Digital Converters (ADCs) with quantization awareness. The proposed algorithm selects the optimal number of antenna elements based on the resolution of ADCs without compromising on the quality of reception. The performance of the proposed algorithm shows significant improvement when compared with conventional and random antenna selection methods.