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With the arrival of next generation wireless communication, a growing numberof new applications like internet of things, autonomous driving systems, anddrone are crowding the unlicensed spectrum. Licensed network such as thelong-term evolution (LTE) also comes to the unlicensed spectrum for betterproviding high-capacity contents with low cost. However, LTE was not designedto share resources with others. Previous solutions usually work on fixedscenarios. This work features a Nonparametric Bayesian reinforcement learningalgorithm to cope with the coexistence between Wi-Fi and LTE licensed assistedaccess (LTE-LAA) agents in 5 GHz unlicensed spectrum. The coexistence problemis modeled as a decentralized partially-observable Markov decision process(Dec-POMDP) and Bayesian inference is adopted for policy learning withnonparametric prior to accommodate the uncertainty of policy for differentagents. A fairness measure is introduced in the reward function to encouragefair sharing between agents. Variational inference for posterior modelapproximation is considered to make the algorithm computationally efficient.Simulation results demonstrate that this algorithm can reach high value withcompact policy representations in few learning iterations.

Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence