This paper deals with the transmission power control problem in wireless networks. Such a problem represents a well known and relevant issue as it allows to efficiently manage the network's required energy and the interference experienced by end-users. With the widespread diffusion of smart devices, the relevance of this aspect further increased and has been identified as such also in 5G standards. The problem has been formalized as a Multi-Agent Reinforcement Learning approach (MARL) to guarantee scalability and robustness. These two aspects also drove the development of an original Distributed Average-Cost Temporal-Difference (TD) Learning algorithm. To adopt such an algorithm, a Markov Game formulation of the power control problem has also been derived. The effectiveness of the proposed distributed framework in reducing the total network's transmission power has been proved by means of simulations in a specific case study.
2022, 2022 IEEE World AI IoT Congress, AIIoT 2022, Pages 393-399
A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks (04b Atto di convegno in volume)
Ornatelli A., Giuseppi A., Tortorelli A.
Gruppo di ricerca: Networked Systems