Channel Knowledge Map-assisted Dual-domain Tracking and Predictive Beamforming for High-Mobility Wireless Networks

TL;DR

This work tackles robust target and beam tracking in high-mobility wireless networks by introducing a Channel Knowledge Map (CKM) that bridges coordinate and beam domains. It develops a CKM-assisted dual-domain tracking framework composed of an EKF-based Coordinate-Domain (C-Domain) tracker and a Beam-Domain (B-Domain) tracker, with CKM serving as both a measurement model in LoS-absent conditions and a prior for AoA transitions. A CRB-based predictive beamforming (BF) and power-allocation scheme is proposed to minimize the maximum AoA estimation error across all paths, leveraging predicted AoAs from the B-Domain. Simulations show that CKM-enabled dual-domain tracking yields substantially better target and AoA tracking than state-of-the-art baselines, especially for NLoS paths, and that predictive BF further enhances performance under high mobility, underpinning CKM’s potential for ISAC and 6G systems.

Abstract

This paper introduces a novel channel knowledge map (CKM)-assisted dual-domain tracking and predictive beamforming scheme for high-mobility wireless networks. The central premise is that the CKM integrates both the coordinate and beam domains, thereby enabling tracking in one domain via treating the other domain's input as priors or measurements. In the coordinate domain (C-Domain), an extended Kalman filter (EKF) is employed to predict and track the state (i.e., location and velocity) of a moving communication receiver across time slots under both line-of-sight (LoS)-present and LoS-absent conditions, where the CKM provides a prior mapping from multipath channel parameters to potential target locations. In the beam domain (B-Domain), the updated location of the receiver is fed back to CKM to offer a priori information of angle of arrival (AoA) variations, which are incorporated to establish beam transition models for effective beam tracking, depending on the angular variation situation of each path. Then, we analyze the Cramér-Rao Bound (CRB) for AoA estimation for each path in the considered system and propose a jointly predictive beamforming and power allocation design to minimize AoA estimation errors, directly enhancing multipath beam tracking accuracy and indirectly improving target tracking performance. Simulation results demonstrate that the proposed scheme achieves significant improvements in both target and beam tracking performance compared to the state-of-the-art approaches, particularly in AoA tracking of non-line-of-sight (NLoS) paths, highlighting the potential gain of CKM in facilitating both target and beam tracking in high-mobility communications.

Figures (11)

• Figure 1: Beam tracking system model for a high-mobility vehicle network in multipath environments.
• Figure 2: CKM-assisted dual-domain tracking framework.
• Figure 3: Angular TPM for the $i$-th path.
• Figure 4: Angular variation over time for each path.
• Figure 5: Average AoA tracking errors with respect to $c_{\Pi_i}[\eta]$ for all paths and slots with $\sigma_z^2=10^{-6} \mathrm{~W}$, $N_t=N_r=32$, and $p_{\text{blk}}=0.15$.