Suitable is the best: Least absolute deviation algorithm under high-mobility non-Gaussian noise environments

Guan Gui, Li Xu, Fumiyuki Adachi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2014 International Workshop on High Mobility Wireless Communications, HMWC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages27-32
Number of pages6
ISBN (Electronic)9781479956463
DOIs
Publication statusPublished - 2014 Dec 30
Event2014 International Workshop on High Mobility Wireless Communications, HMWC 2014 - Beijing, China
Duration: 2014 Nov 12014 Nov 3

Publication series

Name2014 International Workshop on High Mobility Wireless Communications, HMWC 2014

Other

Other2014 International Workshop on High Mobility Wireless Communications, HMWC 2014
CountryChina
CityBeijing
Period14/11/114/11/3

Keywords

  • Non-Gaussian environment
  • high-mobility communications
  • impulisve interference
  • least absolute deviation (LAD)
  • sparse chanenl estimation

ASJC Scopus subject areas

  • Computer Networks and Communications

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