Sensing, Signals, and Communication Seminar with Jean-Francois Chamberland, Texas A&M

Coding and Compressed Sensing for Unsourced Multiple Access
Currently deployed wireless access systems based on sustained connectivity, channel estimates, and scheduling policies are ill-equipped to deal with the sporadic traffic generated by legions of unattended wireless devices. This impending technological challenge has fueled several recent research initiatives whose shared goal is to ready wireless infrastructures for the demands of tomorrow. Pertinent recent advances in this area include the introduction of unsourced, uncoordinated multiple-access models attuned to machine-driven communications and the assessment of their fundamental limits for messages with small payloads. This presentation will review recent contributions on this topic and focus on a novel communication scheme, termed coded compressed sensing, for unsourced multiple-access communication. The proposed divide-and-conquer approach leverages recent progress in compressed sensing and forward error correction to produce a novel uncoordinated access paradigm, along with a computationally efficient decoding algorithm. Within this framework, every active device partitions its data into several sub-blocks and, subsequently, adds redundancy using a systematic linear block code. Compressed sensing techniques are then employed to recover sub-blocks up to a permutation of their order, and the original messages are obtained by stitching fragments together using a tree-based algorithm.