TY - GEN
T1 - Noisy 1-Bit Compressed Sensing with Heterogeneous Side-information
AU - Kafle, Swatantra
AU - Wimalajeewa, Thakshila
AU - Varshney, Pramod K.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - We consider the problem of sparse signal reconstruction from noisy 1-bit compressed measurements using a statistically dependent signal, as an aid. We assume that this signal does not share joint sparse representation with the sparse signal and call it a heterogeneous side-information. We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip errors after quantization. We propose a generalized approximate message passing-based algorithm for signal reconstruction from noisy 1-bit compressed measurements which leverages the dependence between the signal and the heterogeneous side-information. We model the dependence between signal and heterogeneous side-information using copula functions and show, through numerical experiments, that the proposed algorithm yields a better reconstruction performance than 1-bit CS-based recovery algorithms that do not exploit the side-information.
AB - We consider the problem of sparse signal reconstruction from noisy 1-bit compressed measurements using a statistically dependent signal, as an aid. We assume that this signal does not share joint sparse representation with the sparse signal and call it a heterogeneous side-information. We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip errors after quantization. We propose a generalized approximate message passing-based algorithm for signal reconstruction from noisy 1-bit compressed measurements which leverages the dependence between the signal and the heterogeneous side-information. We model the dependence between signal and heterogeneous side-information using copula functions and show, through numerical experiments, that the proposed algorithm yields a better reconstruction performance than 1-bit CS-based recovery algorithms that do not exploit the side-information.
KW - 1-bit compressed measurements
KW - approximate message passing
KW - heterogeneous side-information
KW - sparse signal reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85068987343&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068987343&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682936
DO - 10.1109/ICASSP.2019.8682936
M3 - Conference contribution
AN - SCOPUS:85068987343
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4873
EP - 4877
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -