Dynamic channel selection with reinforcement learning for cognitive WLAN over fiber
International Journal of Communication Systems
Green Access Point Selection for Wireless Local Area Networks Enhanced by Cognitive Radio
Mobile Networks and Applications
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Cognitive wireless local area network (WLAN) over fiber (CWLANoF), which employs remote antenna units (RAUs) connected to a central cognitive access point, can provide a cost-effective and efficient architecture for devices to equally share the industrial, scientific, and medical band by taking advantage of cognitive radio capabilities. Based on the CWLANoF architecture, we propose two methods to reduce collisions among WLAN stations, with multiple independent channels operating at each RAU, and transmission and reception diversity through cooperation of adjacent RAUs. We evaluate the proposed methods through Monte-Carlo simulations. Compared with a conventional WLAN Extended Service Set with two access points, the proposed methods achieve 10% gain on Transmission Control Protocol (TCP) throughput and 10~25% reduction on packet error rate of constant-bit-rate traffic streams for spatially uniform traffic. When the traffic is spatially non-uniform with hotspots arising at certain locations, the TCP throughput gain increases by up to 26%. The results show the potential of CWLANoF in coping with dynamic traffic that often occurs in the indoor wireless access.