Resource Allocation in Macrocell-Femtocells Networks

Abstract

In this thesis, we study the problem of resource allocation in two-tier cellular networks. Base station selection together with dual bandwidth and power allocation among the two tiers are investigated under two different scenarios: dedicated spectrum usage and shared spectrum usage. In the former approach, each tier uses different sub-channels of the available spectrum. Thus, cross-tier interference is completely eliminated. In the latter approach, the available spectrum is being shared among both tiers. Hence, interference mitigation techniques are required. To achieve fair and efficient resource optimization, our model assumes that hybrid access mode is applied in the femtocells. Hybrid access mode is beneficial for system performance as 1) it lessens interference caused by nearby public users due to their high transmission power during the connection to the macrocell, 2) it allows public users to connect to near femtocells and get better Quality of Service (QoS) and 3) it increases system capacity as it allows the macrocell to serve more users. However, femtocells’ owners can behave selfishly by denying public access to avoid any performance reduction in subscribers transmissions. Such a problem needs a motivation scheme to assure the cooperation of femtocells owners. To optimize the resource allocation problem, we propose to use the Genetic Algorithm (GA) to perform power and bandwidth assignments under both dedicated and shared spectrum usage scenarios. The objective of the formulated optimization problem is the maximization of network throughput that is calculated by means of Shannon’s Capacity Law. Moreover, we propose a game-theoretical approach to motivate femtocells’ owners to run their cells on hybrid access mode. In the proposed method, a reputation-based Titfor- Tat strategy is implemented to model the game between femtocells’ owners where each owner observers the behaviors of his opponents and decide whether to provide public access or not accordingly. Simulations were conducted for each part of the research and compared to a modified version of the Weighted Water Filling (WWF) algorithm. Results showed that the proposed GA-based model outperforms the WWF algorithm in terms of resources utilization and system performance. Moreover, the proposed game-theoretical selfishness prevention approach enhanced system performance and significantly reduced interference.

Date of Award	2013
Original language	American English
Supervisor	Hadi Otrok (Supervisor)