Codebook-Based Beam Tracking for Conformal ArrayEnabled UAV MmWave Networks

TL;DR

This work tackles the problem of reliable mmWave inter-UAV communications under 3D mobility by integrating a cylindrical conformal array (CA) on each UAV and designing a dedicated hierarchical codebook to realize fast, subarray-aware beam tracking. It introduces a codebook-based SPAS framework for both t-UAVs and the r-UAV, leveraging subarray activation patterns tied to angular directions, and employs Gaussian process-based UAV motion-state prediction to bound tracking errors. A tracking-error-aware 3D beamwidth control mechanism adapts the subarray size to maintain robust beam gains in dynamic scenarios. Simulation results show that CA-based beam tracking significantly improves spectral efficiency and outage performance compared to UPAs, with TE-aware beamwidth control offering additional robustness and efficiency gains in highly mobile UAV networks.

Abstract

Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned aerial vehicle (UAV) networks. However, as the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the three-dimensional (3D) mobility and attitude variation of UAVs. Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks. More specifically, the key contributions of our work are three-fold. 1) A new mmWave beam tracking framework is established for the CA-enabled UAV mmWave network. 2) A specialized hierarchical codebook is constructed to drive the directional radiating element (DRE)-covered cylindrical conformal array (CCA), which contains both the angular beam pattern and the subarray pattern to fully utilize the potential of the CA. 3) A codebook-based multiuser beam tracking scheme is proposed, where the Gaussian process machine learning enabled UAV position/attitude predication is developed to improve the beam tracking efficiency in conjunction with the tracking-error aware adaptive beamwidth control. Simulation results validate the effectiveness of the proposed codebook-based beam tracking scheme in the CA-enabled UAV mmWave network, and demonstrate the advantages of CA over the conventional planner array in terms of spectrum efficiency and outage probability in the highly dynamic scenarios.

Figures (14)

• Figure 1: The DRE-covered CCA in 3D view and top-view.
• Figure 2: The analog RF precoder structure with dynamic subarrays.
• Figure 3: The considered CC-enabled UAV mmWave network consists of a r-UAV and multiple t-UAVs. UAV position-attitude prediction is performed to obtain the future motion state information (MSI) before next information feedback. The CCA and the beam are shown in detail in the CCA view.
• Figure 4: The proposed 3D CCA hierarchical codebook. The hierarchical codebook contains multiple layers with different beamwidth. The $(i,j)$-th code of the $(m,n)$-th layer contains the AWV $\boldsymbol{v}$ and the corresponding subarray $\mathcal{S}$. The beam coverages of $\boldsymbol{v}(m_s,n_s,i,j,\mathcal{S})$ in azimuth angle and elevation angle are $[(i-1){BW}_{\text{a}},i{BW}_{\text{a}}]$ and $[(j-1){BW}_{\text{e}},j{BW}_{\text{e}}]$, respectively.
• Figure 5: Polar plots of the $(4,32)$-th layer of the codebook coverage on the azimuth plane with $BW_{\text{a,array}}=\frac{\pi}{16}$, $\Delta\alpha=\frac{2\pi}{3}$ and $\Delta\beta=\pi$.

Theorems & Definitions (6)

• Theorem 1
• Theorem 2
• Theorem 3
• Remark 1
• Theorem 4
• Remark 2