A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban Environments

• URL: http://arxiv.org/abs/2607.16930v1
• Date: Sat, 18 Jul 2026 18:48:53 GMT
• Title: A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban Environments
• Abstract summary: Tiered Multi-Agent System (TMAS) dynamically routes telemetry to Domain Micro-Agents, validated on a dataset of 48,618 samples collected in Sunway City, Malaysia.<n>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.
• Score: 1.2599533416395763
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles [51.449502491030756]
  We present a multi-task semantic communication framework for satellite-assisted autonomous driving.<n>The framework is trained end-to-end to jointly optimize both tasks.<n>Results show that the proposed approach achieves significant bandwidth reduction of up to 87.23% to 98.17%.
  arXiv  Detail & Related papers  (2026-07-15T06:49:41Z)
• Spatiotemporal Heterogeneity of AI-Driven Traffic Flow Patterns and Land Use Interaction: A GeoAI-Based Analysis of Multimodal Urban Mobility [0.0]
  Urban traffic flow governed by the complex nonlinear interaction between land use and mobility demand.<n>This study proposes a GeoAI Hybrid framework to model traffic flow patterns and their interaction with land use across three mobility modes.<n>Applying the framework to a dataset of 350 traffic analysis zones across six cities spanning two contrasting urban morphologies, four findings emerge.
  arXiv  Detail & Related papers  (2026-03-05T18:45:44Z)
• Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks [77.17664010626726]
  We focus on the routing with multiple UAV clusters in low-altitude intelligent networks (LAINs)<n>To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques.<n>We show that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T04:30:35Z)
• Spatiotemporal Decision Transformer for Traffic Coordination [1.2099551931618155]
  MADT (Multi-Agent Decision Transformer) is a novel approach that reformulates multi-agent traffic signal control as a sequence modeling problem.<n>Our approach enables offline learning from historical traffic data, with architecture design that facilitates potential online fine-tuning.
  arXiv  Detail & Related papers  (2026-02-02T23:19:13Z)
• Agentic World Modeling for 6G: Near-Real-Time Generative State-Space Reasoning [70.56067503630486]
  We argue that sixth-generation (6G) intelligence is not fluent token prediction but calibrated the capacity to imagine and choose.<n>We show that WM-MS3M cuts mean absolute error (MAE) by 1.69% versus MS3M with 32% fewer parameters and similar latency, and achieves 35-80% lower root mean squared error (RMSE) than attention/hybrid baselines with 2.3-4.1x faster inference.
  arXiv  Detail & Related papers  (2025-11-04T17:22:22Z)
• Enhanced Urban Traffic Management Using CCTV Surveillance Videos and Multi-Source Data Current State Prediction and Frequent Episode Mining [3.3248768737711045]
  This research aims to develop a unified framework that integrates CCTV surveillance videos with multi-source data to enhance real-time urban traffic prediction.<n>The framework was evaluated on the CityFlow dataset comprising 313,931 bounding boxes across 46 cameras.
  arXiv  Detail & Related papers  (2025-10-04T06:11:34Z)
• MSGAT-GRU: A Multi-Scale Graph Attention and Recurrent Model for Spatiotemporal Road Accident Prediction [0.0]
  We propose MSGAT-GRU, a graph attention and recurrent model that captures localized and long-range spatial dependencies.<n>On the Hybrid Beijing Accidents dataset, MSGAT-GRU achieves an RMSE of 0.334 and an F1-score of 0.878, consistently outperforming strong baselines.<n>These results position MSGAT-GRU as a scalable and generalizable model for intelligent transportation systems.
  arXiv  Detail & Related papers  (2025-09-22T14:05:23Z)
• Confidence-Regulated Generative Diffusion Models for Reliable AI Agent Migration in Vehicular Metaverses [55.70043755630583]
  vehicular AI agents are endowed with environment perception, decision-making, and action execution capabilities.<n>We propose a reliable vehicular AI agent migration framework, achieving reliable dynamic migration and efficient resource scheduling.<n>We develop a Confidence-regulated Generative Diffusion Model (CGDM) to efficiently generate AI agent migration decisions.
  arXiv  Detail & Related papers  (2025-05-19T05:04:48Z)
• MULTI-LF: A Unified Continuous Learning Framework for Real-Time DDoS Detection in Multi-Environment Networks [1.5922526181364094]
  Existing AI-based detection systems struggle to adapt to new attack strategies and lack real-time attack detection capabilities.<n>This study proposes an online, continuous learning methodology for DDoS detection in M-En networks.
  arXiv  Detail & Related papers  (2025-04-15T19:44:53Z)
• iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning [57.24340061741223]
  We introduce a distributed multi-agent reinforcement learning (MARL) algorithm that can predict trajectories and intents in dense and heterogeneous traffic scenarios. Our approach for intent-aware planning, iPLAN, allows agents to infer nearby drivers' intents solely from their local observations.
  arXiv  Detail & Related papers  (2023-06-09T20:12:02Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.