Robust Gridless Estimation of Angles and Delays for Full-Dimensional Wideband mmWave Channels

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Robust Gridless Estimation of Angles and Delays for Full-Dimensional Wideband mmWave Channels

By: 
Ting-Ming Yang; Yuan-Pei Lin

In this paper, we consider robust channel estimation for a millimeter wave (mmWave) massive MIMO system with uniform planar arrays (UPA). For many gridless angle estimation methods of mmWave channels, the channel gains needs to be time-invariant during training. We propose a gridless method that is applicable to time-invariant and time-varying channels, and the proposed method is robust to channel variations. This is done by employing time-varying precoders and combiners that are banded block Toeplitz (BBT) matrices. The advantage of time-varying BBT precoders is two-fold. First, with time-varying BBT precoding we can fabricate time-varying channels. Thus the channel can be estimated as if it is a time-varying channel whether the actual channel is time-varying or not. We will show that, thanks to fabricated time variation in the channel, a small training overhead can be used. Secondly, BBT precoding and combining preserve the shift invariance property of the received data matrix. Due to the preservation of shift invariance property, we can employ most existing direction finding methods for estimating the angles and delays, including low complexity gridless subspace based algorithms. Simulation results are given to show that the proposed time-varying BBT precoding provides robust and accurate channel estimates with a low complexity.

Introduction

High data rates have been achieved in mmWave communication systems [1] by employing a large number of antennas for beamforming. Due to heavy path loss, an mmWave channel typically consists of a few dominant paths. The estimation of an mmWave channel becomes a problem of finding the angles and gains associated with the paths. Experiments have shown that the angles of mmWave channels experience large-scale fading whereas the path gains experience small-scale fading [2]. In other words, AoA and AoD are relatively stationary, but the path gains can vary faster, leading to a time-varying channel.

SPS on Twitter

  • DEADLINE EXTENDED: The 2023 IEEE International Workshop on Machine Learning for Signal Processing is now accepting… https://t.co/NLH2u19a3y
  • ONE MONTH OUT! We are celebrating the inaugural SPS Day on 2 June, honoring the date the Society was established in… https://t.co/V6Z3wKGK1O
  • The new SPS Scholarship Program welcomes applications from students interested in pursuing signal processing educat… https://t.co/0aYPMDSWDj
  • CALL FOR PAPERS: The IEEE Journal of Selected Topics in Signal Processing is now seeking submissions for a Special… https://t.co/NPCGrSjQbh
  • Test your knowledge of signal processing history with our April trivia! Our 75th anniversary celebration continues:… https://t.co/4xal7voFER

IEEE SPS Educational Resources

IEEE SPS Resource Center

IEEE SPS YouTube Channel