PROJECT TITLE :

On the Use of Learning Automata in Tuning the Channel Split Ratio of WiMAX Networks

ABSTRACT:

The Worldwide Interoperability for Microwave Access (WiMAX) family of standards have introduced a flexible, economical, and robust wireless interface. Among different attention-grabbing options, WiMAX access networks bring into play a versatile determination of the ratio between the downlink and uplink directions, permitting a relation width from 3 : one to one : one, respectively. However, this promising feature is not properly utilised, since hitherto scheduling and mapping schemes proposed neglect it. In this paper, this difficult issue is effectively addressed by proposing an adaptive model that attempts to adequately alter the downlink-to-uplink subframe width ratio according to the current traffic conditions. In the context of a mobile WiMAX wireless access network, the bottom station is enhanced with a blunder-aware learning automaton so as to be able to identify the magnitude of the incoming and outgoing traffic flows and, in flip, to suitably outline the ratio on a frame-by-frame basis. The model designed is extensively evaluated beneath realistic and dynamic situations, and also the results indicate that its performance is clearly improved compared with schemes having predefined and fastened ratio values.