Accurate Network Traffic Matrix Prediction via LEAD: a Large Language Model-Enhanced Adapter-Based Conditional Diffusion Model

2026-01-29

AI Summary

This work addresses the challenges of dynamic, nonlinear, and bursty characteristics in network traffic matrix forecasting, where traditional discriminative models often suffer from over-smoothing and lack the ability to model uncertainty. To overcome these limitations, the authors propose LEAD, a novel generative framework that uniquely integrates large language models (LLMs) with a conditional diffusion mechanism. The approach encodes traffic matrices as RGB images and leverages a frozen LLM augmented with trainable adapters to efficiently extract temporal semantics. A dual-conditional guidance strategy steers the diffusion process to enhance prediction fidelity. Evaluated on the Abilene and GEANT datasets, LEAD achieves state-of-the-art performance, reducing 20-step prediction RMSE to 0.1134 and 0.0258—improvements of 45.2% and 27.3% over the best baseline—while simultaneously delivering high accuracy, computational efficiency, and uncertainty awareness.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Driven by the evolution toward 6G and AI-native edge intelligence, network operations increasingly require predictive and risk-aware adaptation under stringent computation and latency constraints. Network Traffic Matrix (TM), which characterizes flow volumes between nodes, is a fundamental signal for proactive traffic engineering. However, accurate TM forecasting remains challenging due to the stochastic, non-linear, and bursty nature of network dynamics. Existing discriminative models often suffer from over-smoothing and provide limited uncertainty awareness, leading to poor fidelity under extreme bursts. To address these limitations, we propose LEAD, a Large Language Model (LLM)-Enhanced Adapter-based conditional Diffusion model. First, LEAD adopts a"Traffic-to-Image"paradigm to transform traffic matrices into RGB images, enabling global dependency modeling via vision backbones. Then, we design a"Frozen LLM with Trainable Adapter"model, which efficiently captures temporal semantics with limited computational cost. Moreover, we propose a Dual-Conditioning Strategy to precisely guide a diffusion model to generate complex, dynamic network traffic matrices. Experiments on the Abilene and GEANT datasets demonstrate that LEAD outperforms all baselines. On the Abilene dataset, LEAD attains a remarkable 45.2% reduction in RMSE against the best baseline, with the error margin rising only marginally from 0.1098 at one-step to 0.1134 at 20-step predictions. Meanwhile, on the GEANT dataset, LEAD achieves a 0.0258 RMSE at 20-step prediction horizon which is 27.3% lower than the best baseline.

Problem

• Network Traffic Matrix Prediction
• Stochastic Network Dynamics
• Uncertainty Awareness
• Bursty Traffic
• Time-Series Forecasting

Innovation

• Traffic-to-Image
• LLM-Enhanced Adapter
• Conditional Diffusion Model
• Dual-Conditioning Strategy
• Network Traffic Matrix Prediction