Automated Model Design using Gated Neuron Selection in Telecom
This addresses the need for efficient, compact model design in resource-constrained telecommunications networks, representing an incremental improvement tailored to a specific domain.
The paper tackled the challenge of automating neural network design for telecommunications tasks by introducing TabGNS, a gradient-based NAS method for tabular data, which achieved 51-82% reductions in architecture size and up to 36x faster search times compared to state-of-the-art methods.
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.