A Novel Adaptive Framework for Wireless Push Systems Based on Distributed Learning Automata

A novel adaptive scheme for wireless push systems is presented in this paper. In this wireless environment two entities play the most important role: the server side and the client side that is connected to the system. The server side is responsible to broadcast an item per transmission in order to satisfy the clients’ requests. The performance of the server side depends on item selections. Hence, the server broadcasts an item and the clients are satisfied if the transmitted item was the desired one. In this work, a set of learning automata try to estimate the client demands in a distributed manner. More specifically, an autonomous learning automaton is utilized on each client group, since the clients are gathered into groups based on their location. The output of each automaton is combined in order to produce a well-performed transmission schedule. Concurrently, a round robin phase is adopted, giving the opportunity to the non-popular items to be transmitted. In this manner, the various client demands are treated fairly. The introduced technique is compared with a centralized adaptive scheme and the results indicate that the proposed scheduling framework outperforms the centralized one, in terms of response time and fairness.