  With the arrival of next generation wireless communication, a growing number
of new applications like internet of things, autonomous driving systems, and
drone are crowding the unlicensed spectrum. Licensed network such as the
long-term evolution (LTE) also comes to the unlicensed spectrum for better
providing high-capacity contents with low cost. However, LTE was not designed
to share resources with others. Previous solutions usually work on fixed
scenarios. This work features a Nonparametric Bayesian reinforcement learning
algorithm to cope with the coexistence between Wi-Fi and LTE licensed assisted
access (LTE-LAA) agents in 5 GHz unlicensed spectrum. The coexistence problem
is modeled as a decentralized partially-observable Markov decision process
(Dec-POMDP) and Bayesian inference is adopted for policy learning with
nonparametric prior to accommodate the uncertainty of policy for different
agents. A fairness measure is introduced in the reward function to encourage
fair sharing between agents. Variational inference for posterior model
approximation is considered to make the algorithm computationally efficient.
Simulation results demonstrate that this algorithm can reach high value with
compact policy representations in few learning iterations.
