Traditional vs Deep Learning Algorithms in Telecom Industry -- Cloud Architecture and Algorithm Categorisation

The unprecedented growth of mobile devices, applications and services pose have placed utmost demands on mobile and wireless networking infrastructure. Rapid research and development of 5G systems, have found ways to support mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Moreover inference from heterogeneous mobile data from distributed devices experience challenges due to computational and battery power limitations. As a result models employed in the edge-based scenario are constrained to light-weight to achieve a trade-off between model complexity and accuracy. Also model compression, pruning, and quantization are largely in place.