  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, the adversarial regime
where all channels are suffered by adversarial jamming attack, the mixed
stochastic and adversarial regime where a subset of channels are attacked and
the contaminated stochastic regime where occasionally adversarial events
contaminate the stochastic channel process, etc. To reduce the implementation
time and space complexity, we further develop an enhanced algorithm by
exploiting the internal structure of the selection of channel access strategy.
We conduct extensive simulations in all these regimes to validate our
theoretical analysis. The quantitative performance studies indicate the
superior throughput gain and the flexibility of our algorithm in practice,
which is resilient to both oblivious and adaptive jamming attacks with
different intelligence and any attacking strength that ranges from no-attack to
the full-attack of all spectrum resources.
