Decentralized resource assignment in cognitive networks based on swarming mechanisms over random graphs

This paper proposes a distributed resource assignment strategy for cognitive networks mimicking a swarm foraging mechanism, assuming that the communication among the cognitive nodes is impaired by random link failures and quantization noise. Using results from stochastic approximation theory, we propose a swarm mechanism that converges almost surely to a final allocation even in the presence of imperfect communication scenarios. The theoretical findings are corroborated by numerical results showing that the only effect of the random link failures is to decrease the convergence rate of the algorithm. We propose then a fast swarming approach, robust to random disturbances, that adapts its behavior with respect to the interference power perceived by every node, thus increasing the speed of convergence and improving the resource allocation capabilities.