A game theoretic approach for optimizing density of remote radio heads in user centric cloud-based radio access network. In: 2015 IEEE Global Communications Conference (GLOBECOM 2015
ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website.
TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract-In this paper, we develop a game theoretic formulation for empowering cloud enabled HetNets with adaptive Self Organizing Network (SON) capabilities. SON capabilities for intelligent and efficient radio resource management is a fundamental design pillar for the emerging 5G cellular networks. The C-RAN system model investigated in this paper consists of ultra-dense remote radio heads (RRH) overlaid by central baseband units that can be collocated with much less densely deployed overlaying macro base stations (BS). It has been recently demonstrated that under a user centric scheduling mechanism, C-RAN inherently manifests the trade-off between Energy Efficiency (EE) and Spectral Efficiency (SE) in terms of RRH density. The key objective of the game theoretic framework developed in this paper is to dynamically optimize the trade-off between the EE and the SE of the C-RAN. More specifically, for an ultra-dense C-RAN based HetNet, the density of active RRHs should be carefully dimensioned to maximize the SE. However, the density of RRHs which maximizes the SE may not necessarily be optimal in terms of the EE. In order to strike a balance between these two performance determinants, we develop a game theoretic formulation by employing a Nash bargaining framework. The two metrics of interest, SE and EE, are modeled as virtual players in a bargaining problem and the Nash bargaining solution for RRH density is determined. In the light of the optimization outcome we evaluate corresponding key performance indicators through numerical results. These results offer insights for a C-RAN designer on how to optimally design a SON mechanism to achieve a desired trade-off level between the SE and the EE in a dynamic fashion.