CRLB Calculations for Joint AoA, AoD and Multipath Gain Estimation in Millimeter Wave Wireless Networks

TL;DR

This paper analyzes the theoretical lower bounds on the accuracy of jointly estimating angles and multipath gains in millimeter-wave wireless networks, providing insights into optimal estimation strategies.

Contribution

It introduces a comprehensive CRLB analysis for joint AoA, AoD, and multipath gain estimation in mmWave channels, including Bayesian bounds and numerical evaluations.

Findings

Bayesian CRLB decreases with higher Rician K-factor.

Quantized beamforming codebooks improve estimation bounds.

CRLB analysis guides optimal estimation in mmWave systems.

Abstract

In this report we present an analysis of the non-random and the Bayesian Cramer-Rao lower bound (CRLB) for the joint estimation of angle-of-arrival (AoA), angle-of-departure (AoD), and the multipath amplitudes, for the millimeter-wave (mmWave) wireless networks. Our analysis is applicable to multipath channels with Gaussian noise and independent path parameters. Numerical results based on uniform AoA and AoD in $[0,\pi)$, and Rician fading path amplitudes, reveal that the Bayesian CRLB decreases monotonically with an increase in the Rice factor. Further, the CRLB obtained by using beamforming and combining code books generated by quantizing directly the domain of AoA and AoD was found to be lower than those obtained with other types of beamforming and combining code books.