GTDM-CSAT: an LTE-U self Coexistence Solution based on Game Theory and Reinforcement Learning

  • Pedro Maia de Santana Samsung R&D Institute (SIDIA)
  • José Martins Castro Neto Federal University of Rio Grande do Norte
  • Fuad M. Abinader Jr. Nokia Bell Labs
  • Vicente A. de Sousa Jr. Federal University of Rio Grande do Norte


There is substantial literature covering both problems and solutions related to the operation of Long Term Evolution (LTE) networks in unlicensed spectrum (LTE-U) while in coexistence with other technologies, such as Wi-Fi. However, a seldom explored scenario is the coexistence between multiple LTE-U networks. Within this scenario, a big issue is establishing optimal configurations that take into account fairness among different operators coexisting in the same unlicensed spectrum coverage area. Solutions to this problem could, for instance, react to changes in the environment by "tuning" different system configurations. We propose a game theoretical reinforcement learning algorithm, called GTDM-CSAT, aiming to maximize the LTE-U aggregated throughput while keeping channel access fairness among different access points. GTDM-CSAT uses the relative data rate offered by the system to adapt the LTE-U ON-OFF time . For this, we formulate the problem as a Markovian game, where the LTE-U operators coexist on a two-zero-sum game. The solution for the best ON-OFF time ratio is defined by applying a modified Minimax Q-learning algorithm for finding the game equilibrium. We perform simulations following 3GPP specifications using the ns-3 simulator for evaluating GTDM-CSAT under different traffic load scenarios. Results indicate gains in the system aggregated throughput, and improved performance regarding individual data rate by each operator.

How to Cite
de Santana, P., Castro Neto, J., Abinader Jr., F., & de Sousa Jr., V. (2019). GTDM-CSAT: an LTE-U self Coexistence Solution based on Game Theory and Reinforcement Learning. Journal of Communication and Information Systems, 34(1), 169-177.
Regular Papers