title: Deep neural network based resource allocation in D2D wireless networks
metadata.name: dc.contributor.author
metadata.value: Sun, Qiushi
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Zhang, Yuyi
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Wu, Haitao
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Petrosian, Ovanes L.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2024-02-19T14:00:44Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2024-02-19T14:00:44Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2023-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Sun Q., Zhang Y., Wu H., Petrosian O. L. Deep neural network based resource allocation in D2D wireless networks. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2023, vol. 19, iss. 4, pp. 529–539. https://doi.org/10.21638/11701/spbu10.2023.409
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2023.409
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/44910
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: 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.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: This work was supported by the St. Petersburg State University (ID project: 94062114).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 19; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: multiple-input multiple-output systems
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: deep neural networks
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: heuristics
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: particle swarm optimization
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Deep neural network based resource allocation in D2D wireless networks
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 09.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/44910/1/09.pdf
resources.format: Adobe PDF
resources.size: 758,02 kB