Elastic net for channel estimation in massive MIMO

Ture Peken, Ravi Tandon, Tamal Bose

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Next generation wireless systems will support higher data rates, improved spectral efficiency, and less latency. Massive multiple-input multiple-output (MIMO) is proposed to satisfy these demands. In massive MIMO, many benefits come from employing hundreds of antennas at the base station (BS) and serving dozens of user terminals (UTs) per cell. As the number of antennas increases at the BS, the channel becomes sparse. By exploiting sparse channel in massive MIMO, compressive sensing (CS) methods can be implemented to estimate the channel. In CS methods, the length of pilot sequences can be shortened compared to pilot- based methods. In this paper, a novel channel estimation algorithm based on a CS method called elastic net is proposed. Channel estimation accuracy of pilot-based, lasso, and elastic- net based methods in massive MIMO are compared. It is shown that the elastic-net based method gives the best performance in terms of error for the less pilot symbols and SNR values.

Original languageEnglish (US)
JournalProceedings of the International Telemetering Conference
StatePublished - 2017
EventInternational Telemetering Conference 2017, ITC 2017 - Las Vegas, United States
Duration: Oct 23 2017Oct 26 2017


  • Channel estimation
  • Compressive sensing
  • Elastic net
  • Massive MIMO

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Instrumentation
  • Computer Networks and Communications
  • Signal Processing


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