Cognitive Radio Algorithms Coexisting in a Network: Performance and Parameter Sensitivity

This paper studies the performance of cognitive radios in a scenario where different pairs of radios adopt different cognition/decision making approaches. We want to assess: 1) if there is a category of cognitive radio algorithms that consistently outperforms the others and 2) how sensitive different algorithms are to suboptimal parameter setting. Our approach is to take a representative set of well-known classes of cognitive radio algorithms, mix and match them throughout thousands of simulations, and determine which seem to perform better. We find that choosing a cognitive radio algorithm means finding a balance between the best-case performance obtained by optimally setting all parameters, and the behavior in uncontrolled, unknown environments, where suboptimal decisions are likely to be made. The approaches we consider, namely reinforcement learning, optimization metaheuristics, multi-armed bandit solutions, and supervised learning, greatly differ in their performance. For example, schemes that are able to achieve a high throughput in our simulation study are more sensitive to suboptimally set parameters.