Hybrid Deep Reinforcement Learning for Joint Resource Allocation in Multi-Active RIS-Aided Uplink Communications
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918264180834304 |
|---|---|
| author | Shalma, Mohamed Maher, Engy Aly El-Mahdy, Ahmed |
| author_facet | Shalma, Mohamed Maher, Engy Aly El-Mahdy, Ahmed |
| contents | Active Reconfigurable Intelligent Surfaces (RIS) are a promising technology for 6G wireless networks. This paper investigates a novel hybrid deep reinforcement learning (DRL) framework for resource allocation in a multi-user uplink system assisted by multiple active RISs. The objective is to maximize the minimum user rate by jointly optimizing user transmit powers, active RIS configurations, and base station (BS) beamforming. We derive a closed-form solution for optimal beamforming and employ DRL algorithms: Soft actor-critic (SAC), deep deterministic policy gradient (DDPG), and twin delayed DDPG (TD3) to solve the high-dimensional, non-convex power and RIS optimization problem. Simulation results demonstrate that SAC achieves superior performance with high learning rate leading to faster convergence and lower computational cost compared to DDPG and TD3. Furthermore, the closed-form of optimally beamforming enhances the minimum rate effectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22107 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Deep Reinforcement Learning for Joint Resource Allocation in Multi-Active RIS-Aided Uplink Communications Shalma, Mohamed Maher, Engy Aly El-Mahdy, Ahmed Signal Processing Active Reconfigurable Intelligent Surfaces (RIS) are a promising technology for 6G wireless networks. This paper investigates a novel hybrid deep reinforcement learning (DRL) framework for resource allocation in a multi-user uplink system assisted by multiple active RISs. The objective is to maximize the minimum user rate by jointly optimizing user transmit powers, active RIS configurations, and base station (BS) beamforming. We derive a closed-form solution for optimal beamforming and employ DRL algorithms: Soft actor-critic (SAC), deep deterministic policy gradient (DDPG), and twin delayed DDPG (TD3) to solve the high-dimensional, non-convex power and RIS optimization problem. Simulation results demonstrate that SAC achieves superior performance with high learning rate leading to faster convergence and lower computational cost compared to DDPG and TD3. Furthermore, the closed-form of optimally beamforming enhances the minimum rate effectively. |
| title | Hybrid Deep Reinforcement Learning for Joint Resource Allocation in Multi-Active RIS-Aided Uplink Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2512.22107 |