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

arXiv: https://arxiv.org/abs/2607.16930 · HTML: https://mlsanity.com/paper/2607.16930/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 40 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Builds on (most cited first, 2 of 2)

- [Improving UE Energy Efficiency through Network-aware Video Streaming over 5G](https://mlsanity.com/paper/2302.06004/) (2023)
- [A Kalman Filter based Low Complexity Throughput Prediction Algorithm for 5G Cellular Networks](https://mlsanity.com/paper/2307.04819/) (2023)

## Abstract (arXiv)

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.