Compressed Sensing Channel Estimation for OFDM with Non-Gaussian Multipath Gains

Felipe Gomez-Cuba, Andrea J. Goldsmith

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


This paper analyzes the impact of non-Gaussian multipath component (MPC) amplitude distributions on the performance of Compressed Sensing (CS) channel estimators for OFDM systems. The number of dominant MPCs that any CS algorithm needs to estimate in order to accurately represent the channel is characterized. This number relates to a Compressibility Index (CI) of the channel that depends on the fourth moment of the MPC amplitude distribution. A connection between the Mean Squared Error (MSE) of any CS estimation algorithm and the MPC amplitude distribution fourth moment is revealed that shows a smaller number of MPCs is needed to well-estimate channels when these components have large fourth moment amplitude gains. The analytical results are validated via simulations for channels with lognormal MPCs such as the NYU mmWave channel model. These simulations show that when the MPC amplitude distribution has a high fourth moment, the well known CS algorithm of Orthogonal Matching Pursuit performs almost identically to the Basis Pursuit De-Noising algorithm with a much lower computational cost.

Original languageEnglish (US)
Article number8844996
Pages (from-to)47-61
Number of pages15
JournalIEEE Transactions on Wireless Communications
Issue number1
StatePublished - Jan 2020
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


  • MMSE
  • Multi-path fading
  • sparse OFDM channel estimation


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