Bio-inspired collaborative spectrum sensing and allocation for cognitive radios


Bio-galvanized techniques, together with firefly algorithm, fish school search, and particle swarm optimisation, are utilised during this study to evaluate the optimal weighting vectors employed in the data fusion centre. This analysis is performed for more realistic signals that suffer from non-linear distortions, caused by the facility amplifiers. The obtained optimal weighting vectors are then used for collaborative spectrum sensing and spectrum allocation in cognitive radio networks. Numerical results show that bio-galvanized techniques outperform the traditional algorithms used for spectrum sensing and allocation by deriving optimal weights that ensure the highest price of chance of detection and guarantee the maximum proportional truthful reward for users.

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