Performance Analysis and Optimal Cooperative Cluster Size for Randomly Distributed Small Cells Under CAN Protocol

Abstract

The ease of imposing multicell coordination mechanisms to enhancing system spectrum efficiency (SE) and performance estimate is one of the key advantages of cloud/centralized control area networks (CAN). Enormous number of cooperative cells should theoretically result in a higher SE, but they may also cause considerable delays due to extra channel state information (CSI) feedback and joint processing computing needs at the cloud data centre, resulting in performance deficit. I partition the network into numerous clusters of cooperating tiny cells and create a throughput optimization problem to study the impact of delays on throughput gains. As a function of cluster size, I figure various delay factors and the network's sum-rate, treating cluster size as the fundamental optimization variable. For both linear and planar network installations, I treat both base station and user geometric locations as random variables in my study. On the basis of the homogeneous Poisson point processing (PPP)model, the output SINR (signal-to-interference- plus-noise ratio) and ergodic sum-rate are calculated. The sum-rate optimization problem is formulated and solved in terms of cluster size. The suggested analytical framework may be used to precisely evaluate the performance of practical cloud-based small cell networks via clustered cooperation, according to simulated study.