Abstract
Bio-inspired techniques, including firefly algorithm, fish school search, and particle swarm optimisation, are utilised in this study to evaluate the optimal weighting vectors used in the data fusion centre. This evaluation is performed for more realistic signals that suffer from non-linear distortions, caused by the power 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-inspired techniques outperform the conventional algorithms used for spectrum sensing and allocation by deriving optimal weights that ensure the highest value of probability of detection and guarantee the maximum proportional fair reward for users.
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