The telecommunications industry is experiencing rapid growth in adopting deep learning for critical tasks such as traffic prediction, signal strength prediction, and quality of service optimisation. However, designing neural network architectures for these applications remains challenging and time-consuming, particularly when targeting compact models suitable for resource-constrained network environments. Therefore, there is a need for automating the model design process to create high-performing models efficiently. This paper introduces TabGNS (Tabular Gated Neuron Selection), a novel gradient-based Neural Architecture Search (NAS) method specifically tailored for tabular data in telecommunications networks. We evaluate TabGNS across multiple telecommunications and generic tabular datasets, demonstrating improvements in prediction performance while reducing the architecture size by 51-82% and reducing the search time by up to 36x compared to state-of-the-art tabular NAS methods. Integrating TabGNS into the model lifecycle management enables automated design of neural networks throughout the lifecycle, accelerating deployment of ML solutions in telecommunications networks.

翻译：电信行业正经历着深度学习在流量预测、信号强度预测和服务质量优化等关键任务中应用的快速增长。然而，为这些应用设计神经网络架构仍然具有挑战性且耗时，特别是在针对资源受限的网络环境设计紧凑模型时。因此，需要自动化模型设计过程以高效创建高性能模型。本文介绍了TabGNS（表格门控神经元选择），这是一种专门为电信网络中的表格数据定制的新型基于梯度的神经架构搜索方法。我们在多个电信和通用表格数据集上评估TabGNS，结果表明，与最先进的表格NAS方法相比，该方法在提升预测性能的同时，将架构大小减少了51-82%，并将搜索时间减少了高达36倍。将TabGNS集成到模型生命周期管理中，能够实现神经网络在整个生命周期内的自动化设计，从而加速机器学习解决方案在电信网络中的部署。