Extended Kalman Filter Beam Tracking for Millimeter Wave Vehicular Communications

TL;DR

This paper introduces an Extended Kalman Filter-based beam tracking algorithm for millimeter-wave vehicular communications, significantly improving tracking accuracy while considering vehicle kinematics.

Contribution

It proposes a novel EKF scheme using position, velocity, and channel states, with explicit Jacobian derivation, enhancing beam tracking efficiency in vehicular environments.

Findings

49% reduction in mean square error

Low computational complexity

Effective in fast-changing vehicular scenarios

Abstract

Millimeter-wave (mmWave) communication is a promising technology to meet the ever-growing data traffic of vehicular communications. Unfortunately, more frequent channel estimations are required in this spectrum due to the narrow beams employed to compensate for the high path loss. Hence, the development of highly efficient beam tracking algorithms is essential to enable the technology, particularly for fast-changing environments in vehicular communications. In this paper, we propose an innovative scheme for beam tracking based on the Extended Kalman Filter (EKF), improving the mean square error performance by 49% in vehicular settings. We propose to use the position, velocity, and channel coefficient as state variables of the EKF algorithm and show that such an approach results in improved beam tracking with low computational complexity by taking the kinematic characteristics of the system into account. We also explicitly derive the closed-from expressions for the Jacobian matrix of the EKF algorithm.