Performance Improvement of Cell Free Massive MIMO System Using User Clustering and Access Point Selection Technique

Abstract A Cell Free massive MIMO, comprising a very large number of distributed access points (APs) is a promising technology to provide high data rate, spectral efficiency (SE), and energy efficiency (EE). The system performance of cell free M-MIMO is optimal when selecting access points (AP) from a large number of APs. In this work, zero-forcing (ZF) and minimum means square error (MMSE) linear precoding are used since it is free from self-interference hence, improving the system sum-rate. Here, the maximum channel gain-based Access Point Selection (APS) algorithm is introduced for access point (AP) selection in the Cell free M-MIMO network to enhance the system data-rate. APS algorithm used to select those APs whose channel gain is highest therefore it improves the rate of the system. The same number of users is scheduled using a simple semi-orthogonal user scheduling (SUS) algorithm. We also used a user clustering algorithm for grouping the users around the AP to schedule the best users. It is observed from the results that the APS and SUS algorithm jointly improve the system rate significantly in cell free massive MIMO systems.