Interference coordination strategy based on Nash bargaining for small‐cell networks. Issue 13 (1st September 2015)

• Record Type: Journal Article
• Authors: Yu, Guanding; Xu, Yang; Yin, Rui; Qu, Fengzhong
• Is Part Of: IET communications. Volume 9:Issue 13(2015)
• Page Start: 1583
• Page End: 1590
• Publication Date: 2015-09-01
• Subjects: cochannel interference -- cellular radio -- game theory -- resource allocation -- nonlinear programming -- integer programming -- genetic algorithms -- probability; cochannel interference coordination strategy -- Nash bargaining model -- small‐cell networks -- distributed scheme -- resource blocks -- resource allocation -- base stations -- resource bargaining request -- nonlinear integer optimisation -- genetic algorithm‐based solution -- outage probability reduction -- system throughput improvement; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2014.1186
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : In this study, a distributed scheme based on the Nash bargaining model is designed to coordinate co‐channel interference for small‐cell networks. The authors consider a scenario that resource blocks can be reused among different small cells. Different to existing works where resource allocation is conducted at the base stations, they propose the scheme where user initialises resource bargaining request to the serving base station once its quality‐of‐service cannot be satisfied because of the severe co‐channel interference from other users. Since the general bargaining problem is a non‐linear integer optimisation, the genetic algorithm is utilised to solve it. They also develop a low‐complexity bargaining model which only takes into account the strongest co‐channel interference. Simulation results show that the proposed distributed scheme can effectively reduce the outage probability of users and improve the system throughput. In addition, the proposed low‐complexity bargaining solution can achieve a close performance to the genetic algorithm‐based solution.