A Decoupling-based Approach for Signature Estimation of Wideband XL MIMO-FMCW Radars

Modern radars employing wideband signals and extremely large (XL) multiple-input multiple-output (MIMO) arrays can significantly improve range and angular resolution. However, when large bandwidth and array aperture are used simultaneously, the spatial delay across the array becomes comparable to the radar range resolution, leading to the spatial wideband effect (SWE). The SWE introduces several distortions including range migration (range squint), beam squint, and range-angle coupling (RAC), which spread the target response in the range-angle domain and may cause physically separated targets to overlap and mask each other. In this work, we propose a decoupling-based target detection and parameter estimation framework for MIMO frequency modulated continuous wave (FMCW) radar. The proposed method reformulates the joint range-angle estimation problem as a decoupled sequential frequency estimation problem, where the two-dimensional (2D) estimation is carried out through successive one-dimensional (1D) super-resolution estimations. Specifically, we employ orthogonal matching pursuit (OMP) to perform sparse recovery-based range and angle estimation with high resolution. The proposed decoupling strategy is further extended to spatial wideband XL-MIMO FMCW radar systems, enabling reliable detection and separation of targets even when their responses overlap due to severe RAC. Simulation results demonstrate that the proposed approach accurately detects multiple targets and successfully resolves overlapping target responses in the presence of SWE, outperforming conventional Fourier transform and clustering-based methods.

An Artificial Intelligence Enabled Signature Estimation of Dual Wideband Systems in Ultra-Low Signal-to-Noise Ratio

Millimeter-wave (mmWave) massive Multiple Input Multiple Output (MIMO) systems encounter both spatial wideband spreading and temporal wideband effects in the communication channels of individual users. Accurate estimation of a user's channel signature -- specifically, the direction of arrival and time of arrival -- is crucial for designing efficient beamforming transceivers, especially under noisy observations. In this work, we propose an Artificial Intelligence (AI)-enabled framework for estimating the channel signature of a user's location in mmWave massive MIMO systems. Our approach explicitly accounts for spatial wideband spreading, finite basis leakage effects, and significant unknown receiver noise. We demonstrate the effectiveness of a denoising convolutional neural network with residual learning for recovering channel responses, even when channel gains are of extremely low amplitude and embedded in ultra-high receiver noise environments. Notably, our method successfully recovers spatio-temporal diversity branches at signal-to-noise ratios as low as -20 dB. Furthermore, we introduce a local gravitation-based clustering algorithm to infer the number of physical propagation paths (unknown a priori) and to identify their respective support in the delay-angle domain of the denoised response. To complement our approach, we design tailored metrics for evaluating denoising and clustering performance within the context of wireless communications. We validate our framework through system-level simulations using Orthogonal Frequency Division Multiplexing (OFDM) with a Quadrature Phase Shift Keying (QPSK) modulation scheme over mmWave fading channels, highlighting the necessity and robustness of the proposed methods in ultra-low SNR scenarios.

Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems

Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is an stringent requirement for several wireless systems like sonar, radar, communications, and dual-function radar communication (DFRC). Due to the use of high carrier frequency and bandwidth, most of these systems are designed with multiple antennae and subcarriers. Although the resolution is high in the large array regime, the DoA-ToA estimation accuracy of the practical on-grid estimation methods still suffers from estimation inaccuracy due to the spectral leakage effect. In this article, we propose DoA-ToA estimation methods for multi-antenna multi-carrier systems with an orthogonal frequency division multiplexing (OFDM) signal. In the first method, we apply discrete Fourier transform (DFT) based coarse signature estimation and propose a low complexity multistage fine-tuning for extreme enhancement in the estimation accuracy. The second method is based on compressed sensing, where we achieve the super-resolution by taking a 2D-overcomplete angle-delay dictionary than the actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP method, we apply the low complexity 2D-OMP method on the matrix data model that makes the use of CS methods practical in the context of large array regimes. Through numerical simulations, we show that our proposed methods achieve the similar performance as that of the subspace-based 2D-MUSIC method with a significant reduction in computational complexity.