Enterprise AI Analysis
Modified prioritized DDPG algorithm for joint beamforming and RIS phase optimization in MISO downlink systems
This research introduces a Modified Prioritized Deep Deterministic Policy Gradient (MP-DDPG) algorithm for robust beamforming optimization in Reconfigurable Intelligent Surface (RIS)-assisted wireless communication systems. The primary objective is to minimize transmitted power at the eNB by jointly optimizing beamforming at the eNB and the phase shifts of the RIS, while satisfying constraints such as maximum transmit power and QoS requirements of the User Equipment (UE). A key advantage of the proposed DRL solution is its ability to operate without requiring instantaneous CSI acquisition, which is typically complex in RIS-assisted systems. The simulation results consistently demonstrate the superior performance of the MP-DDPG algorithm. Compared to Particle Swarm Optimization (PSO) and conventional DDPG, the MP-DDPG algorithm achieves faster convergence to the minimum transmitted power. Furthermore, it attains a lower minimum saturated transmitted power with a smaller number of RIS elements and eNB antennas, indicating improved performance with reduced system complexity. This performance improvement is attributed to the prioritized experience replay mechanism within MP-DDPG, which selects more effective samples to train the DRL model instead of random selection, leading to a better training process. The findings highlight the potential of MP-DDPG as an efficient and robust solution to optimize resource allocation in future 6G and beyond wireless communication networks.
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Reduced Transmitted Power
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Faster Convergence
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Lower System Complexity
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Problem Statement
MP-DDPG Algorithm
Key Contributions
Joint optimization of beamforming at the Base Station (BS) and Reconfigurable Intelligent Surface (RIS) phases in Multiple Input Single Output (MISO) downlink systems is a non-convex and NP-hard problem. Traditional optimization methods lead to high computational complexity. The primary objective is to minimize transmitted power while adhering to critical constraints such as maximum power limits and Quality of Service (QoS) requirements of the User's Equipment (UE). Additionally, practical challenges such as imperfect Channel State Information (CSI) and signaling overhead need to be addressed.
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Reduction in Transmitted Power with Fewer RIS Elements
MP-DDPG Learning Process
Agent-Environment Interaction & Data Collection
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Store Experience in Replay Buffer
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Assign Priority based on Reward & KNN Distance
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Priority-Based Batch Sampling
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Update Actor-Critic Networks
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Periodically Update Sample Priorities
| Feature | PSO | DDPG | MP-DDPG |
|---|---|---|---|
| Optimization Approach | Heuristic Swarm Intelligence | Deep Reinforcement Learning (Actor-Critic) | DRL with Prioritized Experience Replay |
| Convergence Speed | Slower | Faster than PSO | Significantly Faster |
| Sample Efficiency | N/A | Random Sampling (Less Efficient) | Prioritized Sampling (Highly Efficient) |
| Handling Non-Convexity | Good for certain problems | Effective | Highly Effective |
| System Complexity Reduction | Limited | Moderate | Significant |
Real-world Impact: 6G Wireless Networks
Problem: Future 6G wireless communication demands ultra-reliable low-latency communication (URLLC), massive connectivity for IoT, and enhanced energy efficiency.
Solution: MP-DDPG's joint optimization of BS beamforming and RIS phase shifts, coupled with its ability to operate without instantaneous CSI, provides an adaptive and robust framework. This is crucial for energy-efficient resource allocation, extending coverage, and mitigating signal impairments in complex 6G environments.
Outcome: By minimizing transmitted power while meeting QoS, MP-DDPG enables more sustainable and high-performing 6G networks, supporting novel applications like autonomous driving and remote robotics with reduced operational costs.
The proposed Modified Prioritized Deep Deterministic Policy Gradient (MP-DDPG) algorithm is a DRL-based solution that combines Reinforcement Learning (RL) and Deep Neural Networks (DNN) to solve the non-convex optimization problem. It enhances the standard DDPG by introducing a novel priority metric that combines the distance in time between samples and their K nearest neighbors (KNN samples), and the reward of the sample. This approach improves sample efficiency, convergence stability, and leads to faster convergence and superior performance compared to conventional DDPG and PSO. It's particularly effective for continuous action spaces inherent in beamforming and phase shift control.
The main contributions are:
- Proposed a novel priority metric combining time distance and KNN for prioritized experience replay in DDPG.
- Improved sample efficiency and convergence stability, leading to faster convergence and superior performance in transmitted power minimization.
- Demonstrated robust performance under imperfect CSI and practicality for larger-scale systems with reduced complexity.
- Joint optimization of BS beamforming and RIS phase shifts for energy-efficient wireless communication while adhering to QoS and power constraints.
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