Time-Series Prediction using Nature-Inspired Small Models and Curriculum Learning

As wireless cellular networks evolve from one generation to the next, the associated challenges in terms of compute, latency, and energy efficiency are continuously on the rise. However, traditional methods are unable to cope with the complexity of operation in next generation mobile networks, and the operational costs can become a significant hurdle in emerging generations. To address these issues, an AI-native wireless communication system is needed to support faster processing and optimization. Researchers have been exploring nature-inspired models for years to improve deep learning architectures, making them smaller, sparser, more efficient, and easily interpretable. In this study, we propose a nature-inspired Machine Learning architecture that leverages curriculum learning to predict cellular traffic on real wireless cellular network data. Our proposed architecture requires significantly fewer parameters to train, is 64% sparse and performs better than the standard state-of-the-art model. Here, sparsity refers to the fewer connections than the maximum possible number of connections within that network between each layer. Moreover, in terms of performance and carbon emissions, this sparsely connected architecture is better than traditional Machine Learning models.