In this Intel on AI podcast episode: 4G/ LTE network is a preferred method of information transfer today and is becoming more and more crucial in our extremely connected lives. To keep up with the ever-increasing volume of traffic, the network is constantly changing and becoming more and more complex. Legacy rules-based network automation techniques are not working, and communication service providers need to use predictive health analytics to monitor, predict and optimizing the behavior of 4G/LTE network continuously. Networks need to become 'intelligent' and can take care of themselves?. Subhankar Pal, the Assistant Vice President of Research and Innovation at Altran, joins the Intel on AI podcast to discuss how Altran's NetAnticipate framework is driving state-of-the-art self-learning networks through continuous self-feedback.

Subhankar illustrates how Altran's NetAnticipate uses advanced deep learning (DL) models for channel quality prediction and health analytics of 4G/LTE radio networks. He talks about how the solution involves network behavior prediction using key performance indicators in multi-cell mobility scenarios along with several regression and classification models chained together to achieve its network prediction. Subhankar also describes how the Intel AI Builders team helped with optimization testing of Intel optimized Python and Tensorflow to enable Altran to reduce training time and improve model performance so their customers can use existing Intel based hardware to achieve their network automation. Finally, Subhankar discusses the future of 5G technology and how Altran is enabling the future of LTE networks.

To learn more, visit: altran.com

Visit Intel AI Builders at: builders.intel.com/ai