Adaptive and Fast Combined Waveform-Beamforming Design for mmWave Automotive Joint Communication-Radar

TL;DR

Numerical results demonstrate that the proposed JCR design enables the estimation of short- and medium-range radar channels in the Doppler-angle domain with a low normalized MSE, at the expense of a small degradation in the communication distortion MSE.

Abstract

Millimeter-wave (mmWave) joint communication-radar (JCR) will enable high data rate communication and high-resolution radar sensing for applications such as autonomous driving. Prior JCR systems that are based on the mmWave communications hardware, however, suffer from a limited angular field-of-view and low estimation accuracy for radars due to the employed directional communication beam. In this paper, we propose an adaptive and fast combined waveform-beamforming design for the mmWave automotive JCR with a phased-array architecture that permits a trade-off between communication and radar performances. To rapidly estimate the mmWave automotive radar channel in the Doppler-angle domain with a wide field-of-view, our JCR design employs a few circulant shifts of the transmit beamformer and apply two-dimensional partial Fourier compressed sensing technique. We optimize these circulant shifts to achieve minimum coherence in compressed sensing. We evaluate the JCR performance trade-offs using a normalized mean square error (MSE) metric for radar estimation and a distortion MSE metric for data communication, which is analogous to the distortion metric in the rate distortion theory. Additionally, we develop a MSE-based weighted average optimization problem for the adaptive JCR combined waveform-beamforming design. Numerical results demonstrate that our proposed JCR design enables the estimation of short- and medium-range radar channels in the Doppler-angle domain with a low normalized MSE, at the expense of a small degradation in the communication distortion MSE.

Figures (11)

• Figure 1: An illustration of an automotive mmWave JCR system that simultaneously perform SRR/MRR radar sensing with a wide FoV and V2V communication with a narrow FoV.
• Figure 2: A CPI of $T$ seconds duration with $M$ JCR equi-spaced frames separated by an IFS of $T_\mathrm{IFS}$. Each $L$-symbols frame consists of $L_\mathrm{r} = \rho L_\mathrm{BLK}$ number of preamble symbols and $(L-L_\mathrm{r})$ number of communication data symbols. Each frame is placed at an integer multiple of $T_\mathrm{D}$.
• Figure 3: The JCR TX ULA at the source vehicle in (a) uses all the antennas to generate a narrow coherent beam for communication and distribute the remaining energy uniformly along the other directions for radar sensing. The radar RX ULA at the source vehicle in (b) forms a constant gain beam for radar sensing, while the communication RX ULA at the recipient vehicle in (c) generates a narrow coherent beam pointed towards the JCR source transmitter.
• Figure 4: The sampling trajectory traverses through one element in every row of ${\mathbf{Z}} (\delta)$ for the combined-waveform beamforming design in the CCS-JCR approach.
• Figure 5: An example of the optimized binary sampling matrix and its corresponding PSF for $M=N=31$. The PSF has constant amplitude of $1/\sqrt{31}$ in all the columns except the first one. The first column in the PSF matrix is ${\mathbf{e}}_{0,31}$.