To overcome the problem of existing neighboring access point （AP） discovery methods in WLAN, for example they （PnP） of mul proposed. Us can not provide the accurate neighboring APs information needed for the Plug-and-Play ti-mode APs, three kinds of neighboring AP discovery and information exchange methods are ing these three neighboring AP discovery methods, passive discovery method, active discovery method and station assistant discovery method, the multi-mode AP can discover all neighboring APs and obtain needed information. We further propose two whole process flows, which combine three discovery methods in different manner, to achieve different goals. One process flow is to discover the neighboring AP as fast as possible, called fast discovery process flow. The other is to discover the neighboring AP with minimal interference to neighboring and accuracy of the method is confirmed APs, called the minimal interference process flow. The validity by the simulation.
梁立涛Wang LeiZhang QixunFeng ZhiyongYu YifanBai YongChen Lan
This paper presents the multi-step Q-learning(MQL)algorithm as an autonomic approach to thejoint radio resource management(JRRM)among heterogeneous radio access technologies(RATs)in theB3G environment.Through the'trial-and-error'on-line learning process,the JRRM controller can con-verge to the optimized admission control policy.The JRRM controller learns to give the best allocation foreach session in terms of both the access RAT and the service bandwidth.Simulation results show that theproposed algorithm realizes the autonomy of JRRM and achieves well trade-off between the spectrum utilityand the blocking probability comparing to the load-balancing algorithm and the utility-maximizing algo-rithm.Besides,the proposed algorithm has better online performances and convergence speed than theone-step Q-learning(QL)algorithm.Therefore,the user statisfaction degree could be improved also.
