Data-driven (ML/AI) Communications: Theory to Algorithms (TBD – 2021)

This workshop is aimed at gaining machine learning/artificial intelligence (ML/AI) based insights into highly interference-tolerant, error-resilient as well as bandwidth-efficient waveforms utilizing the dimensions of space, frequency, and time optimizing bits/sec/Hz/m^2.  SII Center planning discussions will focus on centralized, as well as distributed, lightweight algorithms for physical layer beamforming, MAC layer coordination, as well as aggregation across multiple modalities of communications (e.g., WiFi and cellular) beyond the MAC layer.Particular emphasis will be placed on how to plan for use of real-world measurements for design of data-driven communication waveforms that can address interference and coverage issues and radio frequency (RF) sensing.

Workshop Goals:  This workshop is intended to help identify a few grand challenges that hinder the efficient use of sub-6 GHz, mmWave and THz frequencies for pervasive coverage and suggest potential individual NSF research projects that could address those problems.  In addition, the workshop will define possible collaborative mechanisms and resource sharing opportunities for joint projects and industry partnerships that can mutually benefit a future SII Center and the wireless industry.