Adaptive Beam Tracking With the Unscented Kalman Filter for Millimeter Wave Communication

Abstract

Millimeter wave (mmWave) communication links for 5G cellular technology require high beamforming gain to overcome channel impairments and achieve high throughput.While much work has focused on estimating mmWave channels and designing beamforming schemes, the time dynamic nature of mmWave channels quickly renders estimates stale and increases sounding overhead. We model the underlying time dynamic state space of mmWave channels and design sounding beamformers suitable for tracking in a Kalman filtering framework. Given an initial channel estimate, filtering efficiently leads to refined estimates and allows forward prediction for higher sustained beamforming gain during data transmission. From tracked prior channel estimates, adaptively chosen optimal and constrained suboptimal beams reduce sounding overhead while minimizing estimation error.

Allied Power Constraint Optimization and Optimal Beam Tracking Schemes for Mobile mmWave Massive MIMO Communications

“…this confirms that (10) belongs to Lemma 1 and which in turn helps to conduct optimization problem given in (11).…”

“…This scheme not only decreases beam training overhead, but also offers multiple user tracking rather than tracking only one user in a time slot. Researchers in [11] and [12] showed work on extended Kalman filter (EKF) to track channel state based on the linearization mechanism subject to the channel state estimation and the covariance magnitude. But as it is well known that in most of the cases the channel models are nonlinear to match with practical scenarios and the Jacobian matrices correspond to directional sounding beam, hence minimum deviation from main design limitations in the EKF operation may cause unstable performance with large computational overhead.…”

“…For this reason, [13] proposed unscented Kalman filter (UKF) with the intractability of recursive Bayesian estimation that does not depend on a Jacobian matrix and presented that the UKF is a better tracking scheme compared to that of the EKF. According to [14], based on extended Kalman filter (EKF) incorporating a monopulse signal is an effective beam tracking scheme for the moving object in the unmanned aerial vehicles (UAVs) to manage the challenges of mobility and frequent channel disconnection and also shown that it is even better compared to other conventional schemes [15][16][17]. By considering UAV position and attitude estimation with machine learning (ML), an efficient beamwidth control based beam tracking approach was proposed in [18,19] for optimal beam pair selection.…”

Channel Fingerprint Based Beam Tracking for Millimeter Wave Communications

“…Many research efforts have been devoted to designing highefficient beam training schemes, such as using configurable beam width for adaptive beam search [2], sending pseudorandom beacons to apply compressive sensing techniques [3], double-link beam tracking to overcome the blockage problem [4], probabilistic beam tracking for hybrid beamforming architectures [5], adaptive beam tracking with the unscented kalman filter [6] and narrowing down the search range with the historical training results [7]. Channel fingerprints can also This act as useful historical information to aid beam tracking.…”

“…Estimate the gains of the beam configurations not trained according to (12), and select the beamforming configuration i t with the largest gain for data transmission. 6: end for Similar to the RBE based scheme, the EKF based scheme selects the T beam configurations with the largest expected gains at locationx t|t−1 for training:…”