Access point selection mechanism based on cross-layer awareness for cognitive networks

Abstract

Accessing to the optimal network was an effective way of ensuring the efficiency of network resources utilization and improving network performance.An access point selection mechanism based on cross-layer awareness for cognitive networks(CN_CLA)was proposed.Firstly,a cross-layer cognitive framework was constructed for obtaining the primary evaluation parameters that influence the performance of network access.Secondly,the fuzzy theory was applied for evaluating the access network performance comprehensively,and the weights in each layer were optimized using the quantum genetic algorithm,and then the access point was selected intelligently.Simulation results show that the proposed method chooses reasonable access networks without intervention of users.Furthermore,it is superior to the traditional methods,including the link capacity scheme,H_RSSI_S and AS_FTM,in terms of throughput,delay,session completion rate,packet loss and other performance indicators.

Keywords

• cognitive networks;cross-layer awareness;fuzzy synthetic evaluation;quantum genetic algorithm