多智能体系统提升5G吞吐量预测精度，适配城市多运营商复杂场景。
A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban Environments
- 分层多智能体动态分配边缘数据，按场景自动匹配专用代理。
- 预测准确率R²达0.931，误差低至0.53 Mbps，显著优于传统模型。
- 适合5G/6G资源调度、网络优化团队及智能交通系统开发者。
吞吐量预测是人工智能驱动的6G资源编排的基础。传统单一机器学习模型难以在不同运营商、移动模式和业务类型间泛化，导致信号条件与实际吞吐量之间存在显著随机性差距。为解决异构城市环境中的这一问题，我们提出分层多智能体系统（TMAS），将边缘遥测数据动态路由至上下文感知的领域微代理。该系统基于马来西亚双威城采集的48,618条样本数据进行验证，使用Nemo Handy路测软件，涵盖三家一级运营商、三种移动模式（高架人行道、地面接驳巴士、高架快速公交）以及三种业务类型（持续下载、持续上传、自适应视频流）。评估显示，TMAS克服了可预测性瓶颈，达到最高R²为0.931，平均绝对误差（MAE）低至0.53 Mbps。系统具备高运行效率：微代理训练迅速，推理延迟低，智能体路由开销仅为0.004至0.126毫秒，表明其适用于下一代无线网络对响应速度的要求。
原文摘要 · Abstract (English)
Throughput prediction is foundational for artificial intelligence-driven 6G resource orchestration. Conventional monolithic machine learning models struggle to generalize across diverse operators, mobility modes, and traffic types, leaving a critical stochasticity gap between signal conditions and achievable throughput. To overcome these constraints in heterogeneous urban environments, we propose a Tiered Multi-Agent System (TMAS) that dynamically routes edge telemetry to context-aware Domain Micro-Agents, validated on a dataset of 48,618 samples collected in Sunway City, Malaysia, with Nemo Handy drive test software, spanning three Tier-1 mobile network operators, three mobility modes, namely (i) elevated pedestrian walkway, (ii) ground-level shuttle bus, and (iii) elevated bus rapid transit; and three traffic profiles, namely (i) persistent download, (ii) persistent upload, and (iii) adaptive video streaming. Our evaluations reveal that TMAS overcomes predictability bottlenecks, achieving a coefficient of determination (R2) of up to 0.931 and a Mean Absolute Error (MAE) as low as 0.53 Mbps. The system demonstrates high operational efficiency, with rapid micro-agent training times, low inference latencies, and agentic routing overhead of 0.004 to 0.126 ms. These latency characteristics indicate the architecture is a promising candidate for the response times required by next-generation wireless networks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。