Reinforcement Learning with Budget-Constrained Nonparametric Function Approximation for Opportunistic Spectrum Access
This work addresses spectrum efficiency for wireless communication systems, presenting an incremental improvement with a novel sparsification technique.
The paper tackled the problem of maximizing throughput in congested spectrum bands by predicting idle slots for opportunistic access, proposing a kernel-based reinforcement learning method with budget-constrained sparsification that showed performance gains over carrier-sense systems in numerical experiments.
Opportunistic spectrum access is one of the emerging techniques for maximizing throughput in congested bands and is enabled by predicting idle slots in spectrum. We propose a kernel-based reinforcement learning approach coupled with a novel budget-constrained sparsification technique that efficiently captures the environment to find the best channel access actions. This approach allows learning and planning over the intrinsic state-action space and extends well to large state spaces. We apply our methods to evaluate coexistence of a reinforcement learning-based radio with a multi-channel adversarial radio and a single-channel CSMA-CA radio. Numerical experiments show the performance gains over carrier-sense systems.