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.

A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks / Ornatelli, A.; Giuseppi, A.; Tortorelli, A.. - (2022), pp. 393-399. (Intervento presentato al convegno 2022 IEEE World AI IoT Congress, AIIoT 2022 tenutosi a Seattle; USA) [10.1109/AIIoT54504.2022.9817168].

A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks

Ornatelli A.
;
Giuseppi A.
;
Tortorelli A.
2022

Abstract

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
average cost TD Learning; distributed reinforcement learning; dynamic consensus; net-worked multi-agent system; power control
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A Distributed Average Cost Reinforcement Learning approach for Power Control in Wireless 5G Networks / Ornatelli, A.; Giuseppi, A.; Tortorelli, A.. - (2022), pp. 393-399. (Intervento presentato al convegno 2022 IEEE World AI IoT Congress, AIIoT 2022 tenutosi a Seattle; USA) [10.1109/AIIoT54504.2022.9817168].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1654502
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