Centralized versus decentralized multi-cell resource and power allocation for multiuser OFDMA networks

• Published in: Computer communications Vol. 107; pp. 112 - 124
• Main Authors: Yassin, Mohamad, Lahoud, Samer, Khawam, Kinda, Ibrahim, Marc, Mezher, Dany, Cousin, Bernard
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15-07-2017; Elsevier
• Subjects: Computer Science; Convex optimization; ICIC; Inter-cell interference; Networking and Internet Architecture; OFDMA; Resource and power allocation; Resource and power allocation; OFDMA; Convex optimization; ICIC; Inter-cell interference; inter-cell interference; resource and power allocation
• ISSN: 0140-3664; 1873-703X
• DOI: 10.1016/j.comcom.2017.04.002

The exponential growth in the usage of mobile networks along with the increasing number of User Equipments (UEs) are exacerbating the scarcity of frequency resources. Dense frequency reuse on the downlink of multiuser Orthogonal Frequency Division Multiple Access networks leads to severe Inter-Cell Interference (ICI) problems. Resource and power allocation techniques are required to alleviate the harmful impact of ICI. Contrarily to the existing techniques that consider single-cell resource and power allocation problem without taking ICI into account, we formulate a centralized downlink multi-cell joint resource and power allocation problem. The objective is to maximize system throughput while guaranteeing throughput fairness between UEs. We demonstrate that the joint problem is separable into two independent problems: a resource allocation problem and a power allocation problem. Lagrange duality theory is used to solve the centralized power allocation problem. We also tackle the resource and power allocation problem differently by addressing it in a decentralized manner. We propose a non-cooperative downlink power allocation approach based on game theory. The players are the base stations, and each base station seeks to maximize its own utility function. We investigate the convergence of our proposed centralized and decentralized approaches, and we compare their performance with that of state-of-the-art approaches.