Dynamic Spectrum Access in Non-stationary Environments: A DRL-LSTM Integrated Approach
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 International Conference on Computing, Networking and Communications (ICNC), 2023, 00, pp. 159-164
- Issue Date:
- 2023-03-23
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Dynamic_Spectrum_Access_in_Non-stationary_Environments_A_DRL-LSTM_Integrated_Approach.pdf | Published version | 2.49 MB |
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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
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