Over the last decade we have witnessed a series of impressive breakthroughs in data science and AI thanks to important advances in the algorithmic efficiency of data-driven approaches, such as deep learning. While the prime examples of disruptive progress have occurred in fields such as computer vision or natural language processing, there is still a huge potential for these improvements to carry over to other disciplines such as communications engineering. Incipient research has demonstrated the benefits of machine learning for the lower layers of the communication stack, including coding, modulation, equalization, detection, resource allocation, etc.
This training school revolved around the application of Machine and Deep Learning techniques to the design of (beyond) 5G communication systems, with particular emphasis on physical and lower layers. The training school has comprised keynote and tutorial lectures by renowned researchers from academia and industry, focused presentations on recent advances in this research field and hands-on sessions.
The program included a student poster session and a Machine learning challenge, with prizes and awards for both activities.
COINS supported Ashish Rauniyar to attend the 6th Training School on Machine and Deep Learning Techniques for (Beyond) 5G Wireless Communication Systems in Barcelona, Spain.