Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks
Fuente:
arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913734383894528 |
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| author | Malhotra, Shubham Yashu, Fnu Saqib, Muhammad Mehta, Dipkumar Jangid, Jagdish Dixit, Sachin |
| author_facet | Malhotra, Shubham Yashu, Fnu Saqib, Muhammad Mehta, Dipkumar Jangid, Jagdish Dixit, Sachin |
| contents | This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_01129 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks Malhotra, Shubham Yashu, Fnu Saqib, Muhammad Mehta, Dipkumar Jangid, Jagdish Dixit, Sachin Distributed, Parallel, and Cluster Computing Artificial Intelligence Emerging Technologies Machine Learning This report investigates the application of deep reinforcement learning (DRL) algorithms for dynamic resource allocation in wireless communication systems. An environment that includes a base station, multiple antennas, and user equipment is created. Using the RLlib library, various DRL algorithms such as Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) are then applied. These algorithms are compared based on their ability to optimize resource allocation, focusing on the impact of different learning rates and scheduling policies. The findings demonstrate that the choice of algorithm and learning rate significantly influences system performance, with DRL providing more efficient resource allocation compared to traditional methods. |
| title | Deep Reinforcement Learning for Dynamic Resource Allocation in Wireless Networks |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Emerging Technologies Machine Learning |
| url | https://arxiv.org/abs/2502.01129 |