A Reinforcement Learning Framework for Optimizing Throughput in DOCSIS Networks

K. Dugan, Maher Harb, D. Rice
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Abstract

The capacity in a communication network is restricted by the famous Shannon-Hartley theorem, which establishes a relationship between maximum achievable capacity, channel bandwidth, and signal-to-noise ratio of the channel. The state-of-the-art in pushing the achievable capacity close to the theoretical limit revolves around coming up with ever more efficient error correction algorithms combined with assigning the proper modulation and encoding scheme to match the conditions of the spectrum at any given point in time. In cable broadband networks, which operate under the DOCSIS protocol, a Profile Management Application (PMA) system uses telemetry collected from cable modems and cable modem termination systems (CMTSs) to dynamically assign DOCSIS profiles that constitute a combination of Forward Error Correction (FEC) configuration, a Quadrature Amplitude Modulation (QAM) level, and other protocol-based configurations. The objective behind this dynamic assignment is twofold: maximizing capacity and keeping the uncorrectable error rate at a minimal level. The current PMA implementation, adopts a rule-based approach, where pre-defined thresholds govern the decisions for adjusting the profiles. This approach, while proven to be successful, limits opportunities to fully realize optimal DOCSIS configurations to bring system performance closer to the Shannon limit. Through a reinforcement learning (RL) implementation of PMA, it is possible to substitute the pre-defined rules for a system that learns to select the optimal configuration at each decision point, based on past outcomes and potential future rewards. In this paper, we focus on designing an RL-based PMA system to manage DOCSIS 3.0 upstream configurations.
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一种优化DOCSIS网络吞吐量的强化学习框架
通信网络的容量受到著名的香农-哈特利定理的限制,该定理建立了最大可实现容量与信道带宽和信道信噪比之间的关系。将可实现容量推向接近理论极限的最先进技术围绕着提出更有效的纠错算法,并结合分配适当的调制和编码方案来匹配任何给定时间点的频谱条件。在DOCSIS协议下运行的有线宽带网络中,配置文件管理应用程序(PMA)系统使用从电缆调制解调器和电缆调制解调器终端系统(cmts)收集的遥测数据来动态分配构成前向纠错(FEC)配置、正交幅度调制(QAM)级别和其他基于协议配置的组合的DOCSIS配置文件。这种动态分配背后的目标有两个:最大限度地提高容量,并将不可纠正的错误率保持在最低水平。当前的PMA实现采用基于规则的方法,其中预定义的阈值控制调整概要文件的决策。这种方法虽然被证明是成功的,但却限制了充分实现最佳DOCSIS配置的机会,从而使系统性能更接近Shannon极限。通过PMA的强化学习(RL)实现,可以将预定义的规则替换为一个系统,该系统可以根据过去的结果和潜在的未来奖励,在每个决策点学习选择最佳配置。在本文中,我们重点设计了一个基于rl的PMA系统来管理DOCSIS 3.0上游配置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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