A Novel Approach for Joint Radio Resource Management Based on Fuzzy Neural Methodology

In this paper, an innovative mechanism to perform joint radio resource management (JRRM) in the context of heterogeneous radio access networks is introduced. In particular, a fuzzy neural algorithm that is able to ensure certain quality-of-service (QoS) constraints in a multicell scenario deployment with three different radio access technologies (RATs), namely, the wireless local area network (WLAN), the universal mobile telecommunication system (UMTS), and the global system for mobile communications (GSM)/Enhanced Data rates for GSM Evolution (EDGE) radio access network (GERAN), is discussed. The proposed fuzzy neural JRRM algorithm is able to jointly manage the common available radio resources operating in two steps. The first step selects a suitable combination of cells built around the three available RATs, while the second step chooses the most appropriate RAT to which a user should be attached. A proper granted bit rate is also selected for each user in the second step. Different implementations are presented and compared, showing that the envisaged fuzzy neural methodology framework, which is able to cope with the complexities and uncertainties of heterogeneous scenarios, could be a promising choice. Furthermore, simulation results show that the reinforcement learning mechanisms introduced in the proposed JRRM methodology allow guaranteeing the QoS requirement in terms of the so-called user dissatisfaction probability in the presence of different traffic loads and under different dynamic situations. Also, the proposed framework is able to take into consideration different operator policies as well as different subjective criteria by means of a multiple decision-making mechanism, such as balancing the traffic among the RATs or giving more priority to the selection of one RAT in front of another one.

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