Evolutive algorithm for spectral handoff prediction in cognitive wireless networks

Context: Meta-heuristic algorithms have received considerable attention in the solution of spectrum assignment problems since they have proven to be efficient with lower computational load and complexity and fast execution time in complex applications. Genetic algorithms are meta-heuristic optimization models inspired in the process of genetics and evolution that can offer local or global optimal solutions; their implementation in cognitive radio has led to using old techniques, generate new models and formulate hybrid structures. Objective: This work proposes a spectral handoff prediction using evolutive algorithms and the multivariable approach of fuzzy feedback analytic hierarchic process (FFAHP). Method: To develop the proposed model, an availability matrix is generated through a genetic algorithm which is used as training strategy; the evaluation of the algorithm is performed for Wi-Fi technology with a 500-channel spectral occupation of real data and a transmission time of 10 minutes. Results: Results are summarized in five evaluation metrics: average number of accumulated failed handoffs, average number of successful handoffs, average bandwidth, cumulative average delay and cumulative average throughput. 674 Diego Giral, Cesar Hernández and Hans Marquez Conclusion: According to the metrics obtained through simulations, the proposed strategy is efficient and improves performance in terms of spectral mobility in cognitive radio networks.