Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches (1901.07159v1)

Abstract: The model-based power allocation algorithm has been investigated for decades, but it requires the mathematical models to be analytically tractable and it usually has high computational complexity. Recently, the data-driven model-free machine learning enabled approaches are being rapidly developed to obtain near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as of great potential for future intelligent networks. In this paper, the DRL approaches are considered for power control in multi-user wireless communication cellular networks. Considering the cross-cell cooperation, the off-line/on-line centralized training and the distributed execution, we present a mathematical analysis for the DRL-based top-level design. The concrete DRL design is further developed based on this foundation, and policy-based REINFORCE, value-based deep Q learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed. Simulation results show that the proposed data-driven approaches outperform the state-of-art model-based methods on sum-rate performance, with good generalization power and faster processing speed. Furthermore, the proposed DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness, and can be incorporated into existing resource allocation schemes due to its generality.

• The paper demonstrates that DRL algorithms achieve near-optimal power distribution in multi-user cellular networks under challenging interference conditions.
• It compares policy-based, value-based, and actor-critic methods, showing DDPG's superior performance in sum-rate and reduced computational variance.
• The study emphasizes a centralized training and distributed execution framework that enhances scalability and adaptability in dynamic wireless environments.

Power Allocation in Multi-User Cellular Networks: Deep Reinforcement Learning Approaches

This paper investigates the application of deep reinforcement learning (DRL) algorithms for power allocation in multi-user cellular networks, specifically targeting interfering multiple-access channels. In contrast to traditional model-based methods that require analytically tractable models and often exhibit high computational complexity, the authors propose data-driven, model-free approaches utilizing deep reinforcement learning to achieve near-optimal power allocation with reduced computational demands. The paper spans several DRL techniques, with a focus on policy-based REINFORCE, value-based Deep Q Learning (DQL), and the actor-critic Deep Deterministic Policy Gradient (DDPG) algorithms.

Results and Implications

The paper demonstrates through extensive simulations that the proposed DRL methodologies outperform existing model-based strategies, such as fractional programming (FP) and weighted minimum mean squared error (WMMSE), particularly in terms of sum-rate performance and computational efficiency. Notably, the paper highlights the superior performance and robustness of DDPG over other DRL variants, attributing its efficiency to the elimination of quantization errors and its capability for handling continuous action spaces.

The comparison of DRL algorithms reveals that while both DQL and REINFORCE can lead to equivalent theoretical performance when sufficiently trained, in practice, DDPG exhibits advantageous performance metrics and reduced variance in sum-rate. This emphasizes DDPG's potential for broader applicability in dynamic and heterogeneous network environments.

Moreover, the research underscores the practicality of DRL in communication systems, presenting a compelling argument for its utility in accommodating the ever-evolving requirements of modern wireless networks. The inherent ability of DRL frameworks to adapt to fluctuating channel conditions without explicit modeling of complex dynamics underscores its value as a robust tool for resource allocation.

Theoretical Contributions

A significant theoretical contribution of the paper lies in the detailed analysis of appropriate DRL strategies for static optimization problems in cellular networks. The authors provide a clear framework for the centralized training and distributed execution paradigm, which notably reduces the computational burden on centralized entities and enhances scalability. This architectural model is shown to be effective for large-scale deployments, considering inter-cell cooperation and dynamic power allocation challenges.

Future Directions

The paper opens several avenues for future research. The strong performance of DDPG suggests further exploration into other actor-critic methods and the potential integration of function approximation techniques to further enhance decision-making in complex environments. The authors also suggest exploring multi-action scenarios, where DDPG's structure can better accommodate joint optimization tasks across several parallel objectives.

Conclusion

This paper robustly advances the state of power control in wireless communication systems by applying sophisticated DRL algorithms to achieve superior allocation strategies. The convergence of machine learning with communication networks, as demonstrated, provides a promising pathway towards more adaptable and intelligent network management solutions, highlighting the evolving role of DRL in overcoming traditional limitations in network design.