Traditional vs Deep Learning Algorithms in the Telecom Industry

The unprecedented growth of mobile devices, applications and services have placed the utmost demand 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 experiences 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.