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IEEE International Conference on Machine Learning for Communication and Networking
May 5 - May 8
DeepSig is excited to sponsor the IEEE International Conference on Machine Learning for Communication and Networking (IEEE ICMLCN) from May 5 – 8, 2024. This event focuses exclusively on the intersection of AI/ML and communication technologies—a rapidly advancing area of research that promises to redefine the future of communication systems and networks.
Join us at the conference where we’ll actively engage in multiple sessions, sharing our insights and innovations. Our participation highlights include:
Interactive Session 7: Learning to transmit and receive
Date: Tuesday, May 7
Time: 13:30-14:30
Location: Exhibition Area
Featured topic: Deep Learning Based Uplink Multi-User SIMO Beamforming Design
Contributors: Cemil Vahapoglu (University of Maryland, USA); Tim O’Shea (DeepSig Inc & Virginia Tech, USA); Tamoghna Roy (DeepSig Inc., USA); Sennur Ulukus (University of Maryland, USA).
Panel 3: AI-native 6G radio interfaces: What can hardware acceleration really bring out
Date: Tuesday, May 7
Time: 14:30-15:30
Location: Room F2 and Kith Innovation
Abstract: Recent advancements in radio interface technologies offer performance benefits but require complex management solutions, driving the need for increased network automation through AI/ML adoption. 6G wireless networks are expected to integrate AI/ML directly into air interface design, presenting opportunities and challenges in computational intensity, energy consumption, and infrastructure complexity, with significant economic and political implications.
Moderator: Carles Anton-Haro, Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), Spain
Panelists: Dr. Bo Göransson, Ericsson, Sweden; Prof. Deniz Gündüz, Imperial College, London, UK; Dr. Tim O’Shea, DeepSig, USA; Dr. Zoran Zivkovic, Intel, Netherlands
Demo 10: Demonstrating the OmniPHY real-time pre-6G AI-native air-interface over the air
Date: Tuesday, May 7
Time: 12:30-16:30
Location: Digital Futures Hub and Room F2
Abstract: One of today’s most exciting design considerations for the 6G Air Interfaces is moving to an AI-Native approach for the physical layer. Such an approach offers many significant advantages, such as removing pilots, adapting to local channel conditions or hardware impairments, and even cross-layer optimization for semantic communications. To this end, We demonstrate OmniPHY, our AI-Native Air Interface, which can be used today for real-time high-rate over-air tests and validation of pre-6G physical layer techniques.
Demonstrators: Tim O’Shea (DeepSig Inc & Virginia Tech, USA); Dan DePoy (Virginia Tech, USA); Raj Bhattacharjea (DeepSig Inc, USA); Nitin Nair (DeepSig, USA); Jacob Gilbert (DeepSig, Germany); Tamoghna Roy (DeepSig Inc., USA)