MmWave Channel Estimation via Atomic Norm Minimization for Multi-User Hybrid Precoding

TL;DR

This paper introduces a novel mmWave channel estimation method using Atomic Norm Minimization, which overcomes basis mismatch issues of traditional methods, leading to more accurate channel estimates and improved multi-user precoding performance.

Contribution

It formulates mmWave channel estimation as an ANM problem with a continuous dictionary, enabling accurate estimation via SDP without basis mismatch.

Findings

ANM outperforms grid-based CS in accuracy

Improved spectral efficiency in multi-user precoding

Efficient polynomial-time SDP solution

Abstract

To perform multi-user multiple-input and multiple-output transmission in millimeter-wave(mmWave) cellular systems, the high-dimensional channels need to be estimated for designing the multi-user precoder. Conventional grid-based compressed sensing (CS) methods for mmWave channel estimation suffer from the basis mismatch problem, which prevents accurate channel reconstruction and degrades the precoding performance. This paper formulates mmWave channel estimation as an Atomic Norm Minimization (ANM) problem. In contrast to grid-based CS methods which use discrete dictionaries, ANM uses a continuous dictionary for representing the mmWave channel. We consider a continuous dictionary based on sub-sampling in the antenna domain via a small number of radio frequency chains. We show that mmWave channel estimation using ANM can be formulated as a semidefinite programming (SDP) problem, and the user channel can be accurately estimated via off-the-shelf SDP solvers in polynomial time. Simulation results indicate that ANM can achieve much better estimation accuracy compared to grid-based CS, and significantly improves the spectral efficiency provided by multi-user precoding.