A genetic fuzzy contention window optimization approach for IEEE 802.11 WLANs

The role of IEEE 802.11 wireless local area networks (WLANs) become vital due to its low cost deployment and the aspiration to improve its performance has become the need of the day. Among existing challenges the most prominent are success ratio, packet loss ratio, collision rate, fairness index, and energy consumption, advances in wireless technologies stress to surmount these challenges. To attain these, performance enhancement, genetic fuzzy-contention window optimization (GF-CWO) approach, is proposed which combined the fuzzy logic controller (FLC) and genetic algorithm (GA), through this way GA optimally tuned the FLC. For this purpose three alogrithms are proposed, namely Algorithm-1: GF-CWO for WLANs, Algorithm-2: GA for GF-CWO, and Algorithm-3: FLC for GF-CWO. Proposed GF-CWO approach is tested for binary exponential back-off (BEB), selected being a de facto standard algorithm, and for Channel Status based Sliding Contention Window (CS-SCW) algorithm, selected being a fuzzy logic based algorithm, implemented in MATLAB. Recorded the resultant values for 5, 10, 15, 20, 25, 30, 35, 40, 45, 50 nodes in different files for BEB, CS-SCW, and GF-CWO, respectively. Later on these files were used to evaluate success ratio, packet loss ratio, collision rate, fairness index and energy consumption and also generated the results graphically. The results generated through simulated test confirmed that the GF-CWO has effectively enhanced the performance.