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CLuP-Based Dual-Deconvolution in Automotive ISAC Scenarios

Jonathan Monsalve, Kumar Vijay Mishra

TL;DR

This work tackles joint radar target parameter estimation and message recovery in automotive ISAC under a non-blind dual-deconvolution model. It proposes a CLuP-based algorithm augmented with a nuclear-norm constraint on the Hankel-lift of the unknown vector, enabling stable, low-complexity recovery of radar delays/Doppler and communications symbols. The approach is supported by convergence arguments and validated numerically, showing accurate spectral estimation of radar parameters and robust message recovery even in noisy, high-mobility settings, with favorable comparisons to ADMM baselines. The method holds promise for real-time, scalable ISAC deployment in future vehicle networks by exploiting the low-rank and subspace structures inherent in radar and communications components.

Abstract

Accurate target parameter estimation of range, velocity, and angle is essential for vehicle safety in advanced driver assistance systems (ADAS) and autonomous vehicles. To enable spectrum sharing, ADAS may employ integrated sensing and communications (ISAC). This paper examines a dual-deconvolution automotive ISAC scenario where the radar waveform is known but the propagation channel is not, while in the communications domain, the channel is known but the transmitted message is not. Conventional maximum likelihood (ML) estimation for automotive target parameters is computationally demanding. To address this, we propose a low-complexity approach using the controlled loosening-up (CLuP) algorithm, which employs iterative refinement for efficient separation and estimation of radar targets. We achieve this through a nuclear norm restriction that stabilizes the problem. Numerical experiments demonstrate the robustness of this approach under high-mobility and noisy automotive environments, highlighting CLuP's potential as a scalable, real-time solution for ISAC in future vehicular networks.

CLuP-Based Dual-Deconvolution in Automotive ISAC Scenarios

TL;DR

This work tackles joint radar target parameter estimation and message recovery in automotive ISAC under a non-blind dual-deconvolution model. It proposes a CLuP-based algorithm augmented with a nuclear-norm constraint on the Hankel-lift of the unknown vector, enabling stable, low-complexity recovery of radar delays/Doppler and communications symbols. The approach is supported by convergence arguments and validated numerically, showing accurate spectral estimation of radar parameters and robust message recovery even in noisy, high-mobility settings, with favorable comparisons to ADMM baselines. The method holds promise for real-time, scalable ISAC deployment in future vehicle networks by exploiting the low-rank and subspace structures inherent in radar and communications components.

Abstract

Accurate target parameter estimation of range, velocity, and angle is essential for vehicle safety in advanced driver assistance systems (ADAS) and autonomous vehicles. To enable spectrum sharing, ADAS may employ integrated sensing and communications (ISAC). This paper examines a dual-deconvolution automotive ISAC scenario where the radar waveform is known but the propagation channel is not, while in the communications domain, the channel is known but the transmitted message is not. Conventional maximum likelihood (ML) estimation for automotive target parameters is computationally demanding. To address this, we propose a low-complexity approach using the controlled loosening-up (CLuP) algorithm, which employs iterative refinement for efficient separation and estimation of radar targets. We achieve this through a nuclear norm restriction that stabilizes the problem. Numerical experiments demonstrate the robustness of this approach under high-mobility and noisy automotive environments, highlighting CLuP's potential as a scalable, real-time solution for ISAC in future vehicular networks.

Paper Structure

This paper contains 5 sections, 1 theorem, 24 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System model
  3. CLuP-Based Dual-Deconvolution
  4. Numerical Experiments
  5. Summary

Key Result

Proposition 1

Assume the elements of $\mathbf{D}$, $\mathbf{h}_c$ and $\mathbf{s}$ are independent and identically distributed elements drawn from a standard normal distribution and the conditions A1-A2 hold, then eq:clup2 converges to the exact point with high probability as $N\to \infty$.

Figures (4)

  • Figure 1: Illustration of the hybrid monostatic-bistatic setup of the joint radar-communications scenario. The radar transmitter (Tx) mounted on blue car on bottom right sends a signal to detect other vehicles (labeled as 'Target 1', 'Target 2' and so on) in its surroundings. It also acts a common receiver (Rx) for the radar signal reflected off from these targets and the communications message sent by the red car.
  • Figure 2: NMSE in recovering $\mathbf{x}$ for SNR $=37$, $31$, and $28$ dB with varying values of CLuP parameters (a) $c_0$ and (b) $c_1$.
  • Figure 3: (a) CLuP recovered pseudo-spectrum of radar channel compared with true target parameter values for $L=2$ (b) Estimated norm of communications message vector compared with true values for J=2.
  • Figure 4: The probability of successful reconstruction, averaged over 20 trials, with $P = 9$ and $M = 11$ for dual-deconvolution recovery using (a) CLuP and (b) ADMM.

Theorems & Definitions (1)

  • Proposition 1