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.

翻译：在迈向6G和AI原生边缘智能的演进驱动下，网络运营日益需要在严格的计算和延迟约束下进行预测性和风险感知的自适应。网络流量矩阵（TM）作为表征节点间流量的基础信号，是实现主动流量工程的关键。然而，由于网络动态的随机性、非线性和突发性，精确的TM预测仍面临挑战。现有的判别模型常存在过度平滑问题，且不确定性感知能力有限，导致在极端突发流量下的预测保真度不佳。为解决这些局限，我们提出LEAD——一种基于大型语言模型（LLM）增强的适配器条件扩散模型。首先，LEAD采用"流量转图像"范式将流量矩阵转换为RGB图像，通过视觉骨干网络实现全局依赖建模。其次，我们设计了"冻结LLM+可训练适配器"架构，以有限计算成本高效捕捉时序语义。此外，我们提出双重条件策略，精确引导扩散模型生成复杂动态的网络流量矩阵。在Abilene和GEANT数据集上的实验表明，LEAD在所有基线方法中表现最优。在Abilene数据集上，LEAD的RMSE较最佳基线显著降低45.2%，其误差范围从单步预测的0.1098仅微增至20步预测的0.1134。同时在GEANT数据集上，LEAD在20步预测范围内达到0.0258的RMSE，较最佳基线降低27.3%。