RACE: A Rate Adaptive Channel Estimation Approach for Millimeter Wave MIMO Systems

TL;DR

This paper introduces RACE, a novel rate-adaptive channel estimation algorithm for mmWave MIMO systems that dynamically adjusts measurements to efficiently estimate channels with high accuracy.

Contribution

The paper proposes a rate-adaptive estimation method that reduces measurement overhead by adjusting based on error probability, unlike fixed-measurement approaches.

Findings

Significantly fewer measurements needed for accurate estimation.

High estimation accuracy maintained with fewer measurements.

Outperforms existing multi-stage algorithms in simulations.

Abstract

In this paper, we consider 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 develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE). To this end, we design a maximum likelihood (ML) estimator to optimally extract the path information and the associated probability of error from the increasing number of channel measurements. Based on the ML estimator, the algorithm is able to measure the channel using a variable number of beam patterns until the receiver believes that the estimated direction is correct. This is in contrast to the existing mmWave channel estimation algorithms, in which the number of measurements is typically fixed. Simulation results show that the proposed algorithm can significantly reduce the number of channel estimation measurements while still retaining a high level of accuracy, compared to existing multi-stage channel estimation algorithms.