ADMM-Based Solution for mmWave UL Channel Estimation with One-Bit ADCs via Sparsity Enforcing and Toeplitz Matrix Reconstruction

Majdoddin Esfandiari*, Sergiy A. Vorobyov*, Robert W. Heath

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Low-power millimeter wave (mmWave) multi-input multi-output communication systems can be enabled with the use of one-bit analog-to-digital converters. Owing to the extreme quantization, conventional signal processing tasks such as channel estimation are challenging, making uplink (UL) multiuser receivers difficult to implement. To address this issue, we first reformulate the UL channel estimation problem, and then combine the idea of ℓ1 regularized logistic regression classification and Toeplitz matrix reconstruction in a properly designed optimization problem. Our new method is referred to as ℓ1 regularized logistic regression with Toeplitz matrix reconstruction (L1-RLR-TMR). In addition, we develop a computationally efficient alternating direction method of multi-pliers (ADMM)-based implementation for the L1-RLR-TMR method. Numerical results demonstrate the performance of the L1-RLR-TMR method in comparison with other existing methods.

Original languageEnglish
Title of host publicationICC 2023 - IEEE International Conference on Communications
Subtitle of host publicationSustainable Communications for Renaissance
EditorsMichele Zorzi, Meixia Tao, Walid Saad
Number of pages6
ISBN (Electronic)978-1-5386-7462-8
Publication statusPublished - 2023
MoE publication typeA4 Conference publication
EventIEEE International Conference on Communications - Rome, Italy
Duration: 28 May 20231 Jun 2023

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607


ConferenceIEEE International Conference on Communications
Abbreviated titleICC


  • angular domain
  • Multi-user MIMO
  • one-bit ADC
  • Toeplitz matrix reconstruction
  • uplink channel estimation
  • ℓ regularized logistic regression


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