Conference Paper January 2018

Dithered Beamforming for Channel Estimation in Mmwave-Based Massive Mimo

Vlachos, E., Thompson, J.
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Download PDF IEEE Xplore mmWave/THz channel estimation

Abstract

In this work we consider the challenging problem of chan- nel estimation at the receiver of a massive multiple-input multiple-output system with hybrid analog/digital beamform- ing and low-resolution quantization. We propose a dithered beamforming architecture, where random control signals are injected to the analog part of the receiver beamformer and to the analog-to-digital converters to introduce randomness into the signal capturing process and combat the stair-case quanti- zation effects. The statistical properties of the dithered output are captured via an Expectation-Maximization approximation of the maximum a-posteriori estimator. A low-complexity al- gorithm is proposed which exhibits performance close to the oracle-based least-squares estimation of the sparse channel.

Type Conference Paper
Publication 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
DOI 10.1109/icassp.2018.8461911
Topic Channel Estimation for mmWave and THz Massive MIMO
Date January 2018
Research 6G UAVs

Key Contributions

  • Proposed a dithered beamforming architecture by injecting random control signals into the analog beamformer and ADCs to combat quantization effects.
  • Developed a low-complexity Expectation-Maximization algorithm for sparse channel estimation in hybrid beamforming systems with low-resolution ADCs.
  • Demonstrated that the technique effectively mitigates quantization non-linearities and improves channel estimation performance in mmWave massive MIMO systems.

Hybrid analog-digital receiver with phase dithering in the analog RF combiner and quantization dithering before the ADCs.
Hybrid analog-digital receiver with phase dithering in the analog RF combiner and quantization dithering before the ADCs.
This figure shows that the proposed dithered beamforming technique significantly reduces estimation error compared to non-dithered approaches, achieving near-optimal performance with a 1dB improvement for 2- and 3-bit quantization cases. The results demonstrate rapid convergence of the proposed algorithm, approaching near-optimal performance even with an approximate solution for the sparse channel estimation problem.

Research 6G UAVs