Super resolution channel estimation by using spread spectrum signal and atomic norm minimization

Dongshin Yang, Yutaka Jitsumatsu

Research output: Contribution to journalArticle

Abstract

Compressed Sensing (CS) is known to provide better channel estimation performance than the Least Square (LS) method for channel estimation. However, multipath delays may not be resolved if they span between the grids. This grid problem of CS is an obstacle to super resolution channel estimation. An Atomic Norm (AN) minimization is one of the methods for estimating continuous parameters. The AN minimization can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. There are studies showing that the AN minimization method has better resolution than conventional CS methods. In this paper, we propose a channel estimation method based on the AN minimization for Spread Spectrum (SS) systems. The accuracy of the proposed channel estimation is compared with the conventional LS method and Dantzig Selector (DS) of the CS. In addition to the application of channel estimation in wireless communication, we also show that the AN minimization can be applied to Global Positioning System (GPS) using Gold sequence.

Original languageEnglish
Pages (from-to)2141-2148
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE101A
Issue number12
DOIs
Publication statusPublished - Dec 1 2018

Fingerprint

Spread Spectrum
Super-resolution
Channel Estimation
Channel estimation
Compressed sensing
Compressed Sensing
Norm
Least Square Method
Grid
Global Positioning System
Selector
Multipath
Glossaries
Wireless Communication
Gold
Global positioning system
Time Domain
Communication

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this

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