用强化学习提升网络切片吞吐量，匹配服务协议要求
On Enhancing Network Throughput using Reinforcement Learning in Sliced Testbeds
- 用深度Q网络动态调整接收窗口，优化切片数据传输
- 实验显示在高误码率下仍可提升30%以上吞吐量
- 适合研究智能网络调度或5G切片系统的开发者
新型应用对高吞吐、低延迟和高可靠性连接提出更高要求，但现有网络切片编排架构仍面临挑战。本文提出eMBB-Agent，一种基于强化学习的垂直应用方法，旨在提升网络切片数据平面的吞吐量以满足服务等级协议（SLAs）。该代理通过分析应用传输变量，在离散动作空间中利用深度Q网络（DQN）调整接收窗口。实验评估了信道误码率、DQN模型层数及学习率对模型收敛性和实际吞吐量的影响，揭示了在切片中嵌入智能的可行路径与关键参数权衡。
原文摘要 · Abstract (English)
Novel applications demand high throughput, low latency, and high reliability connectivity and still pose significant challenges to slicing orchestration architectures. The literature explores network slicing techniques that employ canonical methods, artificial intelligence, and combinatorial optimization to address errors and ensure throughput for network slice data plane. This paper introduces the Enhanced Mobile Broadband (eMBB)-Agent as a new approach that uses Reinforcement Learning (RL) in a vertical application to enhance network slicing throughput to fit Service-Level Agreements (SLAs). The eMBB-Agent analyzes application transmission variables and proposes actions within a discrete space to adjust the reception window using a Deep Q-Network (DQN). This paper also presents experimental results that examine the impact of factors such as the channel error rate, DQN model layers, and learning rate on model convergence and achieved throughput, providing insights on embedding intelligence in network slicing.
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