Model-Based Deep Reinforcement Learning Framework for Channel Access in Wireless Networks
- Authors
- Park, Jong In; Chae, Jun Byung; Choi, Kae Won
- Issue Date
- 15-Mar-2024
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- actor-critic; Adaptation models; Heuristic algorithms; model-based reinforcement learning; Planning; POMDP; Receivers; Transmitters; wireless channel access; Wireless communication; Wireless sensor networks; world model
- Citation
- IEEE Internet of Things Journal, v.11, no.6, pp 1 - 1
- Pages
- 1
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Internet of Things Journal
- Volume
- 11
- Number
- 6
- Start Page
- 1
- End Page
- 1
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/110346
- DOI
- 10.1109/JIOT.2023.3325575
- ISSN
- 2327-4662
- Abstract
- In this paper, we propose a model-based reinforcement learning (RL) algorithm for wireless channel access. The model-based RL is a relatively new RL paradigm that integrates the concept of the world model into the agent. The world model is built based on the neural network, and is capable of predicting the future trajectories of actions, rewards, and observations. In this paper, we focus on developing a sophisticated world model based on the partially observable Markov decision process (POMDP). The proposed world model can describe the environment in which only the partial observation emitted from the hidden state is available. For establishing the wireless channel access problem, we introduce two separate environments, one of which describes the channel occupancy dynamics and the other governs data traffic arrival patterns. Both environments are modeled by the proposed POMDP-based world model. For designing an agent capable of making a decision on the next action, we propose a planning algorithm, which makes use of the future trajectories generated from the trained world model differently from the existing model-free RL algorithms. We have conducted extensive simulations to verify the performance of the proposed method in various wireless channel access scenarios. IEEE
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