Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence

TL;DR

This paper introduces a Bayesian nonparametric reinforcement learning algorithm for LTE and Wi-Fi coexistence in unlicensed spectrum, enabling adaptive, fair, and efficient spectrum sharing without fixed scenario assumptions.

Contribution

It presents a novel Bayesian nonparametric RL approach modeling coexistence as a Dec-POMDP, allowing flexible, scalable policy learning for heterogeneous wireless networks.

Findings

Achieves high policy value with compact representations.

Maintains computational efficiency with increasing agent sets.

Demonstrates effective spectrum sharing in simulations.

Abstract

With the formation of next generation wireless communication, a growing number of new applications like internet of things, autonomous car, and drone is 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 for sharing spectrum with others. A cooperation center for these networks is costly because they possess heterogeneous properties and everyone can enter and leave the spectrum unrestrictedly, so the design will be challenging. Since it is infeasible to incorporate potentially infinite scenarios with one unified design, an alternative solution is to let each network learn its own coexistence policy. Previous solutions only work on fixed scenarios. In this work a reinforcement learning algorithm is presented to cope with the coexistence between Wi-Fi and LTE agents in 5 GHz unlicensed spectrum. The coexistence problem was modeled as a decentralized partially observable Markov decision process (Dec-POMDP) and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure was introduced in the reward function to encourage fair sharing between agents. The reinforcement learning was turned into an optimization problem by transforming the value function as likelihood and variational inference for posterior approximation. Simulation results demonstrate that this algorithm can reach high value with compact policy representations, and stay computationally efficient when applying to agent set.