Modeling Super-Lightweight Cellular Traffic Prediction via Multiservice and Multimodal Feature Fusion Network

Cellular Traffic Prediction has proven to be a key enabler towards automatic network management. However, to pursue performance improvement, the existing studies mainly focus on developing complex deep neural network models, which suffer from extensive computation cost and large model size inevitably. Such models are quite difficult to be deployed on resource-constrained devices. In this letter, we propose a multiservice and multimodal feature fusion network for super-lightweight cellular network traffic prediction, namely $m^{2}FFNet$ , to address the issue. Briefly speaking, such a network consists of a duel feature extraction channel based on grouped 3D convolution for capturing multiservice feature and multimodal feature (yielded from wavelet transform decomposition), respectively. Simulation results demonstrate that our proposal can achieve comparable prediction accuracy as the state-of-the-art deep learning methods, meanwhile obtaining much less computation burden with rather few model size.