用自然语言控制网络流量预测偏差，实现节能与服务的动态平衡。
BERTO: Intent-Driven Network Time Series Forecasting via Natural Language Operator Preferences
- 通过自然语言指令调节预测偏差，无需重训模型。
- 实测在约1.4kW功率范围内灵活调控，SLA违规波动降低9倍。
- 适合需要动态调整节能与服务质量的5G基站部署场景。
传统蜂窝网络流量预测模型以最小化对称误差为目标，对运营优先级变化不敏感。为此，本文提出基于BERT的BERTO框架，用于蜂窝网络的流量预测与能耗优化。该框架基于Transformer架构，结合平衡损失函数（BLF）与提示条件机制，使同一模型可通过自然语言指令灵活调整预测偏向，实现过度预测或低估的动态切换，从而在不重新训练或修改参数的情况下适应不同预测策略。在真实数据集上的实验表明，BERTO可在约1.4kW的功耗范围内运行，同时将服务等级协议（SLA）违规波动控制在9倍以内，适用于智能无线接入网（RAN）部署。
原文摘要 · Abstract (English)
Traditional cellular traffic forecasting models are optimized for minimizing symmetric errors, leaving them indifferent to shifting operational priorities. To bridge this gap, we introduce BERTO, a BERT-based framework for traffic prediction and energy optimization in cellular networks. Built on transformer architectures, BERTO achieves high prediction accuracy while enabling a single fine-tuned model to operate across multiple forecasting regimes via natural-language operator prompts. By combining a Balancing Loss Function (BLF) with prompt-based conditioning, BERTO adaptively shifts its forecasting bias toward underprediction or overprediction depending on the operator's desired trade-off between power savings and service quality. This allows the same model to dynamically generate different decision-aware forecasts without retraining or modifying model parameters. Experiments on real-world datasets demonstrate that BERTO can operate across a flexible range of approximately 1.4 kW in power consumption while balancing 9x variation in service level agreement (SLA) violations, making it well suited for intelligent RAN deployments.
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