Millimeter Wave MIMO Channel Estimation using Overlapped Beam Patterns and Rate Adaptation

TL;DR

This paper introduces a fast, rate-adaptive mmWave channel estimation method using overlapped beam patterns, significantly reducing measurement time and improving energy efficiency in large antenna array systems.

Contribution

It proposes a novel overlapped beam pattern design and a rate-adaptive algorithm for efficient mmWave channel estimation, enhancing speed and accuracy over existing methods.

Findings

FCE algorithm reduces measurement count compared to non-overlapped designs.

RACE algorithm achieves up to 6dB SNR gain for same PEE.

Derived analytical expressions for PEE and SNR bounds.

Abstract

This paper is concerned with the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement and thus reduce the required channel estimation time compared to the existing non-overlapped designs. We develop a maximum likelihood (ML) estimator to optimally extract the path information from the channel measurements. Then, we propose a novel rate-adaptive channel estimation (RACE) algorithm, which can dynamically adjust the number of channel measurements based on the expected probability of estimation error (PEE). The performance of both proposed algorithms is analyzed. For the FCE algorithm, an approximate closed-form expression for the PEE is derived. For the RACE algorithm, a lower bound for the minimum signal energy-to-noise ratio required for a given number of channel measurements is developed based on the Shannon-Hartley theorem. Simulation results show that the FCE algorithm significantly reduces the number of channel estimation measurements compared to the existing algorithms using non-overlapped beam patterns. By adopting the RACE algorithm, we can achieve up to a 6dB gain in signal energy-to-noise ratio for the same PEE compared to the existing algorithms.