Hierarchical Sparse Recovery from Hierarchically Structured Measurements with Application to Massive Random Access
Gross B, Flinth A, Roth I, Eisert J, Wunder G (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: IEEE Computer Society
Book Volume: 2021-July
Pages Range: 531-535
Conference Proceedings Title: IEEE Workshop on Statistical Signal Processing Proceedings
Event location: Virtual, Rio de Janeiro, BRA
ISBN: 9781728157672
DOI: 10.1109/SSP49050.2021.9513765
Abstract
A new family of operators, dubbed hierarchical measurement operators, is introduced and discussed within the framework of hierarchically sparse recovery. A hierarchical measurement operator is a composition of block and mixing operations. It notably contains Kronecker products as a special case. Results on their hierarchical restricted isometry property (HiRIP) are derived, generalizing prior work on the recovery of hierarchically sparse signals from Kronecker-structured linear measurements. Specifically, these results show that recovery properties of the block and mixing part can be traded against each other. The measurement structure is motivated by a massive random access channel design in communication engineering. Numerical evaluation of user detection rates demonstrate benefits of the theoretical framework.
Involved external institutions
Freie Universität Berlin
Germany (DE)
Chalmers University of Technology / Chalmers tekniska högskola
Sweden (SE)
Germany (DE)
Chalmers University of Technology / Chalmers tekniska högskola
Sweden (SE)
How to cite
APA:
Gross, B., Flinth, A., Roth, I., Eisert, J., & Wunder, G. (2021). Hierarchical Sparse Recovery from Hierarchically Structured Measurements with Application to Massive Random Access. In IEEE Workshop on Statistical Signal Processing Proceedings (pp. 531-535). Virtual, Rio de Janeiro, BRA: IEEE Computer Society.
MLA:
Gross, Benedikt, et al. "Hierarchical Sparse Recovery from Hierarchically Structured Measurements with Application to Massive Random Access." Proceedings of the 21st IEEE Statistical Signal Processing Workshop, SSP 2021, Virtual, Rio de Janeiro, BRA IEEE Computer Society, 2021. 531-535.
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