Massive multiple-input-multiple-output (MIMO) and beamforming are key technologies, which significantly influence on increasing effectiveness of emerging fifth-generation (5G) wireless communication systems, especially mobile-cellular networks. In this case, the increasing effectiveness is understood mainly as the growth of network capacity resulting from better diversification of radio resources due to their spatial multiplexing in macro- and micro-cells. However, using the narrow beams in lieu of the hitherto used cell-sector brings occurring interference between the neighboring beams in the massive-MIMO antenna system, especially, when they utilize the same frequency channel. An analysis of this effect is the aim of this paper. In this case, it is based on simulation studies, where a multi-elliptical propagation model and standard 3GPP model are used. We present the impact of direction and width of the neighboring beams of 5G new radio gNodeB base station equipped with the multi-beam antenna system on the interference level between these beams. The simulations are carried out for line-of-sight (LOS) and non-LOS conditions of a typical urban environment.
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.