ITU AI/ML in 5G Challenge — India Round– Part 2: AI Techniques for Privacy-preserving Remote Medical Diagnosis + Spectrum and Network Resource Sharing in 5G Networks
This talk will focus on two challenges one of which is design and implementation of AI Techniques for privacy-preserving remote medical diagnosis. 5G networks provide a viable platform for enabling medical diagnostic facilities in rural and remote areas by using the techniques to be designed as part of the first challenge. The idea is to create a web-server hosted inference engine that can support remote medical diagnosis using an intuitive UI application installed on a smartphone. The second challenge is developing strategies for spectrum and network resource sharing in 5G networks. It is expected that 5G systems would be capable of employing explosively scalable bandwidths and spectrum efficient schemes for varying applications. However, the need for cognition based intelligent resource sharing and allocation schemes will persist. User resource requirement patterns are difficult to predict, and efficiently utilizing available windows of opportunity / white spaces in spectrum under these variable conditions is a dynamic and complex problem. The goal is Identification of Key variables for Dynamic Spectrum Access and Proposing a Framework using Key Variables to work out a demonstrable framework for resource / spectrum utilization by using ML approaches in conformity with 3GPP architecture.
Speakers, Panelists and Moderators
BREJESH LALLProfessorIndian Institute of TechnologyBrejesh Lall (Ph.D., Indian Institute of Technology, Delhi 1999) is a professor in the Department of Electrical Engineering at the Indian Institute of Technology Delhi in India where he is in-charge of the Samsung Digital innovations Academy. Earlier, he was the head of Bharti School of Telecom Technology and Management and Ericsson 5G center of Excellence at IIT Delhi. His work focuses on signal processing and machine learning for telecommunications and computer vision.