An Analytical Framework for Clustering Mechanism with Nakagami Fading in User-Centric Cloud RAN

User-centric cloud radio access network (C-RAN), unlike conventional cellular communication, is converging toward cloud and separate all baseband signal processing units from the radio access units. Ultra-dense deployment of remote ratio heads (RRHs) constitutes one of the most promising techniques of explosive data growth while imposes an urgent need of realistic and accurate statistical framework to quantify the network performance. In this paper, we consider modeling the channel fading by a Nakagami distribution, which addresses the signal propagation properties more accurately in the C-RAN with lower antennas. Moreover, we develop an analytical framework for a user-centric clustering mechanism which enables a user can be served by the cooperative cluster of RRHs around it. Specifically, we derive a closed form lower bound on the coverage probability and formulate the area spectral efficiency expression using tools from stochastic geometry. Furthermore, we observe that the cluster size is a tunable parameter to the network performance, which can affect RRH and effective user density in opposite directions, hence, there must he an optimal cluster size which maximizes the area spectral efficiency. Simulation results validate the accuracy of our analytical framework and obtain the optimal cluster size. Our mathematical results pave the way to consider the clustering mechanism with Nakagami fading in user-centric C-RAN.