Deep neural network based resource allocation in D2D wireless networks

Qiushi Sun; Yuyi Zhang; Haitao Wu; Ovanes L. Petrosian

doi:10.21638/11701/spbu10.2023.409

Authors

Qiushi Sun St. Petersburg State University, 199034, St. Petersburg, Russian Federation https://orcid.org/0000-0002-6932-1596
Yuyi Zhang St. Petersburg State University, 199034, St. Petersburg, Russian Federation
Haitao Wu St. Petersburg State University, 199034, St. Petersburg, Russian Federation
Ovanes L. Petrosian St. Petersburg State University, 199034, St. Petersburg, Russian Federation

DOI:

https://doi.org/10.21638/11701/spbu10.2023.409

Abstract

The increased complexity of future 5G wireless communication networks presents a fundamental issue for optimal resource allocation. This continuous, constrained optimal control problem must be solved in real-time since the power allocation should be consistent with the instantly evolving channel state. This paper emphasizes the application of deep learning to develop solutions for radio resource allocation problems in multiple-input multiple-output systems. We introduce a supervised deep neural network model combined with particle swarm optimization to address the issue using heuristic-generated data. We train the model and evaluate its ability to anticipate resource allocation solutions accurately. The simulation result indicates that the trained DNN-based model can deliver the nearoptimal solution.

Keywords:

multiple-input multiple-output systems, deep neural networks, heuristics, particle swarm optimization

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