通过深度符号回归实现闭式拥塞控制
Closed-form congestion control via deep symbolic regression
AI总结：
本文提出先用强化学习训练前传网络拥塞控制策略，再通过深度符号回归将策略转化为可解释、可实时推理的闭式表达式，以近似基线性能并便于实际部署。
摘要：
随着移动网络进入5G时代，采用强化学习（RL）算法来应对超低时延和高吞吐量场景中挑战的兴趣日益增加。与此同时，分组化前传网络的出现提出了传统拥塞控制机制无法满足的苛刻要求，凸显了基于RL的拥塞控制算法的潜力。尽管学习针对严格前传要求优化的RL策略是可行的，但在实际部署中采用神经网络模型仍然在实时推理和可解释性方面带来一些挑战。本文提出了一种在保持基线RL策略所提供性能和泛化能力的同时应对这些挑战的方法。该方法包括：（1）通过强化学习训练一个专门针对类前传网络的拥塞控制策略，（2）从基线中收集状态-动作经验，以及（3）对收集到的数据集执行深度符号回归。所提出的过程通过闭式表达式克服了与推理时间限制相关的挑战，这些表达式能够近似基线性能（链路利用率、时延和公平性），并且可以直接用任何编程语言实现。最后，我们分析了这些闭式表达式的内部工作机制。
英文摘要：
As mobile networks embrace the 5G era, the interest in adopting Reinforcement Learning (RL) algorithms to handle challenges in ultra-low-latency and high throughput scenarios increases. Simultaneously, the advent of packetized fronthaul networks imposes demanding requirements that traditional congestion control mechanisms cannot accomplish, highlighting the potential of RL-based congestion control algorithms. Although learning RL policies optimized for satisfying the stringent fronthaul requirements is feasible, the adoption of neural network models in real deployments still poses some challenges regarding real-time inference and interpretability. This paper proposes a methodology to deal with such challenges while maintaining the performance and generalization capabilities provided by a baseline RL policy. The method consists of (1) training a congestion control policy specialized in fronthaul-like networks via reinforcement learning, (2) collecting state-action experiences from the baseline, and (3) performing deep symbolic regression on the collected dataset. The proposed process overcomes the challenges related to inference-time limitations through closed-form expressions that approximate the baseline performance (link utilization, delay, and fairness) and which can be directly implemented in any programming language. Finally, we analyze the inner workings of the closed-form expressions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
