TY - GEN
T1 - 1-Bit Compressed Sensing with Local Sparsity Patterns
AU - Bulusu, Saikiran
AU - Gandikota, Venkata
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 1-bit compressed sensing (1bCS) is a quantized signal acquisition technique to compress high-dimensional sparse signals. The goal in 1bCS is to design sensing matrices A ? Rm×n with the fewest possible rows that enable efficient and accurate recovery of sparse signals x ? Rn from 1-bit measurements of the form sign(Ax). In this work, we leverage the locality in sparsity patterns observed in many real-world datasets to recover the support of signals exhibiting this sparsity pattern. Our results improve the existing bounds on the number of measurements sufficient for support recovery when the non-zero entries of a signal occur within small local neighborhoods.
AB - 1-bit compressed sensing (1bCS) is a quantized signal acquisition technique to compress high-dimensional sparse signals. The goal in 1bCS is to design sensing matrices A ? Rm×n with the fewest possible rows that enable efficient and accurate recovery of sparse signals x ? Rn from 1-bit measurements of the form sign(Ax). In this work, we leverage the locality in sparsity patterns observed in many real-world datasets to recover the support of signals exhibiting this sparsity pattern. Our results improve the existing bounds on the number of measurements sufficient for support recovery when the non-zero entries of a signal occur within small local neighborhoods.
UR - http://www.scopus.com/inward/record.url?scp=85171477014&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171477014&partnerID=8YFLogxK
U2 - 10.1109/ISIT54713.2023.10206856
DO - 10.1109/ISIT54713.2023.10206856
M3 - Conference contribution
AN - SCOPUS:85171477014
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1172
EP - 1177
BT - 2023 IEEE International Symposium on Information Theory, ISIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Symposium on Information Theory, ISIT 2023
Y2 - 25 June 2023 through 30 June 2023
ER -