Towards Optimal Adaptive Wireless Communications in Unknown Environments
Pan Zhou, Tao Jiang

TL;DR
This paper introduces an adaptive channel access algorithm for wireless communications that effectively learns optimal strategies in unknown, stochastic, adversarial, and mixed environments, demonstrating superior performance and resilience.
Contribution
The paper proposes a novel multi-armed bandit based adaptive algorithm with parameter tuning for optimal channel access in diverse unknown environments, including adversarial and stochastic regimes.
Findings
Achieves near-optimal learning performance across four environment regimes.
Demonstrates superior throughput and resilience against jamming attacks.
Reduces implementation complexity through exploiting internal structure.
Abstract
Designing efficient channel access schemes for wireless communications without any prior knowledge about the nature of environments has been a very challenging issue, especially when the channel states distribution of all spectrum resources could be entirely or partially stochastic and/or adversarial at different time and locations. In this paper, we propose an adaptive channel access algorithm for wireless communications in unknown environments based on the theory of multi-armed bandits (MAB) problems. By automatically tuning two control parameters, i.e., learning rate and exploration probability, our algorithms are capable of finding the optimal channel access strategies and achieving the almost optimal learning performance over time under our defined four typical regimes for general unknown environments, e.g., the stochastic regime where channels follow some unknown i.i.d process,…
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