The rise of AI in telecommunications, from optimizing Radio Access Networks to managing user experience, has sharply increased data volumes and training demands. Telecom data is often noisy, high-dimensional, costly to store, process, and label. Despite Ai's critical role, standard workflows still assume all training samples contribute equally. On the other hand, next generation systems require AI models that are accurate, efficient, and sustainable.The paper questions the assumptions of equal importance by focusing on applying and analyzing the roles of individual samples in telecom training and assessing whether the proposed model optimizes computation and energy use. we perform sample-level gradient analysis across epochs to identify patterns of influence and redundancy in model learning. Based on this, we propose a sample importance framework thats electively prioritizes impactful data and reduces computation without compromising accuracy. Experiments on three real-world telecom datasets show that our method [reserves performance while reducing data needs and computational overhead while advancing the goals of sustainable AI in telecommunications.

翻译：人工智能在电信领域的兴起，从优化无线接入网络到管理用户体验，显著增加了数据量和训练需求。电信数据通常具有噪声、高维度、存储成本高、处理和标注代价大的特点。尽管人工智能发挥着关键作用，标准工作流程仍假设所有训练样本贡献均等。另一方面，下一代系统要求人工智能模型具备高精度、高效性和可持续性。本文通过聚焦于应用和分析电信训练中单个样本的作用，并评估所提模型是否优化了计算与能源使用，对样本重要性均等的假设提出质疑。我们跨训练周期执行样本级梯度分析，以识别模型学习中的影响模式和冗余性。基于此，我们提出一种样本重要性框架，该框架有选择地优先处理高影响力数据，并在不牺牲准确性的前提下减少计算量。在三个真实世界电信数据集上的实验表明，我们的方法在保留性能的同时，减少了数据需求和计算开销，并推动了电信领域可持续人工智能目标的实现。