TY - GEN
T1 - Multiple Orthogonal Least Squares for Joint Sparse Recovery
AU - Kim, Junhan
AU - Shim, Byonghyo
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by ‘The Cross-Ministry Giga KOREA Project’ grant funded by the Korea government (MSIT) (No.GK17P0500, Development of Ultra Low-Latency Radio Access Technologies for 5G URLLC Service) and the National Research Foundation of Korea (NRF) grant funded by the Korean government(MSIP) (2016R1A2B3015576)
Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/15
Y1 - 2018/8/15
N2 - Joint sparse recovery aims to reconstruct multiple sparse signals having a common support using multiple measurement vectors (MMV). In this paper, we propose a robust joint sparse recovery algorithm, termed MMV multiple orthogonal least squares (MMV-MOLS). Owing to the novel identification rule that fully exploits the correlation between the measurement vectors, MMV-MOLS greatly improves the accuracy of the recovered signals over the conventional joint sparse recovery techniques. From the simulation results, we show that MMV-MOLS outperforms conventional joint sparse recovery algorithms, in both full row rank and rank deficient scenarios. In our analysis, we show that MMV-MOLS recovers any row K-sparse matrix accurately in the full row rank scenario with m = K + 1 measurements, which is, in fact, the minimum number of measurements to recover a row K-sparse matrix. In addition, we analyze the performance guarantee of the MMV-MOLS algorithm in the rank deficient scenario using the restricted isometry property (RIP).
AB - Joint sparse recovery aims to reconstruct multiple sparse signals having a common support using multiple measurement vectors (MMV). In this paper, we propose a robust joint sparse recovery algorithm, termed MMV multiple orthogonal least squares (MMV-MOLS). Owing to the novel identification rule that fully exploits the correlation between the measurement vectors, MMV-MOLS greatly improves the accuracy of the recovered signals over the conventional joint sparse recovery techniques. From the simulation results, we show that MMV-MOLS outperforms conventional joint sparse recovery algorithms, in both full row rank and rank deficient scenarios. In our analysis, we show that MMV-MOLS recovers any row K-sparse matrix accurately in the full row rank scenario with m = K + 1 measurements, which is, in fact, the minimum number of measurements to recover a row K-sparse matrix. In addition, we analyze the performance guarantee of the MMV-MOLS algorithm in the rank deficient scenario using the restricted isometry property (RIP).
UR - http://www.scopus.com/inward/record.url?scp=85052443429&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2018.8437788
DO - 10.1109/ISIT.2018.8437788
M3 - Conference contribution
AN - SCOPUS:85052443429
SN - 9781538647806
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 61
EP - 65
BT - 2018 IEEE International Symposium on Information Theory, ISIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Information Theory, ISIT 2018
Y2 - 17 June 2018 through 22 June 2018
ER -