TY - GEN
T1 - Suitable is the best
T2 - 2014 International Workshop on High Mobility Wireless Communications, HMWC 2014
AU - Gui, Guan
AU - Xu, Li
AU - Adachi, Fumiyuki
PY - 2014/12/30
Y1 - 2014/12/30
N2 - Underdetermined inverse sparse signal reconstruction problems in the presence of non-Gaussian noise interference are often encountered in high-mobility wireless communications and signal processing. These problems can be solved by finding the minimizer of a suitable objective function which consists of a data-fitting term and a regularization term with different mixed-norms. Based on the Gaussian-noise assumption, two mixed norms (i.e. ℓ2/ℓ1 and ℓ∞/ℓ1) were confirmed as effective as well as stable algorithms for reconstructing sparse signals. However, the two algorithms are unable to reconstruct signal stable under non-Gaussian noise environments. In this paper, we propose a stable least absolute deviation (LAD) algorithm (i.e., ℓ1/ℓ1) for achieving two aspects: exploiting signal sparse structure information as well as mitigating the non-Gaussian noise interference. First of all, regularization parameter of the proposed algorithm is selected via Monte Carlo simulations. Then, experimental results in different non-Gaussian environments are used to demonstrate the effectiveness of the proposed algorithm.
AB - Underdetermined inverse sparse signal reconstruction problems in the presence of non-Gaussian noise interference are often encountered in high-mobility wireless communications and signal processing. These problems can be solved by finding the minimizer of a suitable objective function which consists of a data-fitting term and a regularization term with different mixed-norms. Based on the Gaussian-noise assumption, two mixed norms (i.e. ℓ2/ℓ1 and ℓ∞/ℓ1) were confirmed as effective as well as stable algorithms for reconstructing sparse signals. However, the two algorithms are unable to reconstruct signal stable under non-Gaussian noise environments. In this paper, we propose a stable least absolute deviation (LAD) algorithm (i.e., ℓ1/ℓ1) for achieving two aspects: exploiting signal sparse structure information as well as mitigating the non-Gaussian noise interference. First of all, regularization parameter of the proposed algorithm is selected via Monte Carlo simulations. Then, experimental results in different non-Gaussian environments are used to demonstrate the effectiveness of the proposed algorithm.
KW - Non-Gaussian environment
KW - high-mobility communications
KW - impulisve interference
KW - least absolute deviation (LAD)
KW - sparse chanenl estimation
UR - http://www.scopus.com/inward/record.url?scp=84929439101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929439101&partnerID=8YFLogxK
U2 - 10.1109/HMWC.2014.7000208
DO - 10.1109/HMWC.2014.7000208
M3 - Conference contribution
AN - SCOPUS:84929439101
T3 - 2014 International Workshop on High Mobility Wireless Communications, HMWC 2014
SP - 27
EP - 32
BT - 2014 International Workshop on High Mobility Wireless Communications, HMWC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 1 November 2014 through 3 November 2014
ER -