Power optimization with low complexity using scaled beamforming approach for a massive MIMO and small cell scenario

Present wireless generation is now evolving from 4G to 5G with a large number of clients. Researchers across the globe are working to sustain the quality of service level, while meeting the increasing demand of the clients. Since, the number of clients are increasing, which give arise to a lot of problems like increased interference, complexity and significant amount of power consumption in the processing and transmission. This paper investigates potential improvements in power optimization by modifying the classical macro-cell with massive multiple input multiple output at the mobile tower, which is overlaid with small cell access points. The main aim of the paper is to optimize the utilization of energy, while maintaining the quality of service at the client end and power optimization at the small cell access point, and base station. But along with power optimization, complexity is also a prime objective of concern. Hence for optimizing or minimizing the power, while maintaining low complexity, a new low complexity algorithm is proposed and is compared with a classical relaxed zero-forcing beam forming algorithm and the optimal solution cases. The complexity analysis of this proposed approach has been done on the basis of change in the base stations and the number of UEs surrounding it. The potential merits of this proposed approach for different deployment scenarios, such as an urban macro heterogeneous deployment scenario in the 3GPP LTE Standard and an urban macro, sub-urban macro, and rural macro deployment scenario in the ITU-R M.2135 standard are analyzed by numerical calculations.