Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks (1808.00490v3)

Abstract: This work demonstrates the potential of deep reinforcement learning techniques for transmit power control in wireless networks. Existing techniques typically find near-optimal power allocations by solving a challenging optimization problem. Most of these algorithms are not scalable to large networks in real-world scenarios because of their computational complexity and instantaneous cross-cell channel state information (CSI) requirement. In this paper, a distributively executed dynamic power allocation scheme is developed based on model-free deep reinforcement learning. Each transmitter collects CSI and quality of service (QoS) information from several neighbors and adapts its own transmit power accordingly. The objective is to maximize a weighted sum-rate utility function, which can be particularized to achieve maximum sum-rate or proportionally fair scheduling. Both random variations and delays in the CSI are inherently addressed using deep Q-learning. For a typical network architecture, the proposed algorithm is shown to achieve near-optimal power allocation in real time based on delayed CSI measurements available to the agents. The proposed scheme is especially suitable for practical scenarios where the system model is inaccurate and CSI delay is non-negligible.

• The paper introduces a novel deep Q-learning algorithm for decentralized power control, eliminating the need for instantaneous cross-cell CSI.
• The paper leverages local CSI and neighbor QoS feedback to achieve optimized weighted sum-rate utility while minimizing interference.
• The paper demonstrates that its DRL approach outperforms traditional centralized methods like WMMSE, achieving robust, scalable performance in large networks.

Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networks

The paper by Nasir and Guo addresses the challenging problem of dynamic power allocation in wireless networks through the implementation of multi-agent deep reinforcement learning (DRL). The authors propose a novel approach that eschews the traditional requirement for instantaneous cross-cell channel state information (CSI) and heavy computational resources, thereby enhancing scalability and adaptability in larger network environments.

Core Contributions and Methodology

1. Distributive Execution with Deep Q-Learning: The authors introduce a model-free DRL algorithm based on deep Q-learning for decentralized power control. This solution inherently handles random variations and delays in CSI, which is a significant departure from conventional optimization-based approaches that depend on real-time, cross-network CSI data.
2. Local Information Utilization: Each transmitter makes use of local CSI and quality of service (QoS) information obtained from its neighbors to adjust its own transmit power. This method not only minimizes interference but also optimizes the weighted sum-rate utility, which can be fine-tuned for maximum sum-rate or proportional fairness.
3. Robust and Scalable Algorithm: The complexity of the proposed method does not increase with the size of the network, making it computationally scalable to systems covering large geographic areas, provided the number of links per unit area remains bounded.
4. Centralized Training with Distributed Execution: The authors leverage centralized training for the network's DQNs to optimize learning while using distributed execution, which benefits from collective learning experiences across agents. This centralized training paradigm aids in circumventing the non-stationarity issues typical of multi-agent learning environments.
5. Performance Comparison and Benchmarks: The proposed DRL-based power allocation outperforms or matches the performance of state-of-the-art centralized optimization algorithms such as WMMSE and FP, which rely on full and instantaneous cross-link CSI. The authors have demonstrated the robustness of their approach through various simulations including large networks and multi-user cells.

Implications and Future Directions

The practical implications of this research are substantial, suggesting that DRL can effectively manage radio resources in dynamic and interference-prone wireless networks. By addressing the need for delayed and partial CSI, this approach mitigates the constraints posed by current centralized methods, which are cumbersome for real-time applications.

Looking forward, further exploration could assess more intricate network models, such as those involving mobile nodes or heterogeneous architectures. Additionally, addressing imperfect CSI and incorporating more sophisticated reward mechanisms could refine the DRL approach, paving the way for deployment in real-world scenarios. Exploring decentralized training or regional re-training might also offer insights into improving learning stability and adaptability.

In conclusion, this paper signifies a promising step toward more intelligent, efficient, and scalable power management in wireless communications, heralding potential advancements in how future wireless networks can dynamically allocate resources amidst uncertain environments.