Evaluation of an enhanced intelligent DCA technique for unlicensed WLANs and PAWNs

Over the last years, a number of mechanisms have been proposed for scheduling different types of traffic over base stations-oriented wireless and mobile systems. The majority of these mechanisms focus on access control in the base station-to-mobile units part of the wireless and mobile system. Recent proposals for the unlicensed spectrum in the 5GHz band have redefined the problem, since base stations, operated by different operators in overlapping geographical areas, need access resolution mechanisms to allocate wireless resources. This issue is addressed here, and a novel mechanism for dynamic channel allocation in unlicensed wireless LANs (WLANs) or public area wireless networks (PAWNs) environments is presented. The proposed method exploits the learning automata technique for the efficient allocation of wireless resources in a distributed manner. Nearby base stations that compete to access and reserve time on separate frequencies are driven by the output of a learning automaton, which determines the available carrier illustrating minimal competition. The paper discusses contention resolution disciplines while the learning automaton algorithm as well as its knowledge base structure are also discussed and evaluated.