Cognitive network spectrum allocation based on multi-agent reinforcement learning

Longhai Wang; Luyong Zhang

doi:10.1117/12.2659146

28 October 2022 Cognitive network spectrum allocation based on multi-agent reinforcement learning

Longhai Wang, Luyong Zhang

Proceedings Volume 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022); 1245313 (2022) https://doi.org/10.1117/12.2659146
Event: Third International Conference on Computer Communication and Network Security (CCNS 2022), 2022, Hohhot, China

Abstract

A more important part of the field of deep reinforcement learning is the study of multi-agents, for the specific scenario of multi-cognitive networks, the choice of spectrum will be affected by two parts, a single cognitive network and the access device under the cognitive network. In view of this specific problem, this paper uses the improved multi-agent reinforcement learning to solve, through the use of multiple agents from the user link modeling, can solve the problem of different cognitive networks for action execution when the environmental state is constantly updated, compared with the original algorithm. In the scenario where spectrum allocation is required in multi-cognitive networks, the improved algorithm can better handle the relationship between master and slave users in multiple networks, so that the spectrum utilization and the overall communication performance of the system are further improved.

Citation Download Citation

Longhai Wang and Luyong Zhang "Cognitive network spectrum allocation based on multi-agent reinforcement learning", Proc. SPIE 12453, Third International Conference on Computer Communication and Network Security (CCNS 2022), 1245313 (28 October 2022); https://doi.org/10.1117/12.2659146

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