Dynamic Spectrum Access in Non-stationary Environments: A DRL-LSTM Integrated Approach

Mingjie Feng, Wenhan Zhang, Marwan Krunz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we investigate the problem of dynamic spectrum access (DSA) in non-stationary environments, Where secondary users (SUs) and primary users (PUs) operate over a shared set of orthogonal channels. The non-stationarity is caused by the time-varying PU activity and the coupled channel access strategies of different SUs. Considering such non-stationarity and the channel dynamics, the DSA problem is formulated as a hidden-mode Markov Decision Process (HMMDP), Which can be decomposed into multiple MDPs under different modes. At each time, one of the modes is active, each mode corresponds to a unique MDP. The HMMDP is solved when the active mode is determined and the MDP under this mode is solved. We first propose a deep reinforcement learning (DRL) framework for solving the MDP under a given mode. We then propose a long short-term memory (LSTM)-based approach to predict the active mode at each time slot. Simulation results show that the proposed scheme outperforms benchmark schemes by achieving significantly fewer collisions and improved spectrum utilization.

Original languageEnglish (US)
Title of host publication2023 International Conference on Computing, Networking and Communications, ICNC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages159-164
Number of pages6
ISBN (Electronic)9781665457194
DOIs
StatePublished - 2023
Event2023 International Conference on Computing, Networking and Communications, ICNC 2023 - Honolulu, United States
Duration: Feb 20 2023Feb 22 2023

Publication series

Name2023 International Conference on Computing, Networking and Communications, ICNC 2023

Conference

Conference2023 International Conference on Computing, Networking and Communications, ICNC 2023
Country/TerritoryUnited States
CityHonolulu
Period2/20/232/22/23

Keywords

  • Dynamic spectrum access
  • deep reinforcement learning
  • hidden-mode Markov Decision Process
  • long short-term memory
  • non-stationary environment

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems and Management

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