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Low-Complexity Joint Azimuth-Range-Velocity Estimation for Integrated Sensing and Communication with OFDM Waveform

Jun Zhang, Gang Yang, Qibin Ye, Yixuan Huang, Su Hu

TL;DR

This work addresses the high computational burden of joint azimuth-range-velocity estimation (JARVE) in OFDM-based ISAC systems. It derives the CRBs for JARVE and introduces a low-complexity PI-2DMUSIC approach that replaces exhaustive 3D MUSIC searches with spatial smoothing, a lightweight 3D parameter initialization, and iterative subspace updates using Levenberg–Marquardt refinement followed by MMSE-based 3D pairing. The proposed method achieves substantial complexity reductions (up to thousands of times faster than 3D-MUSIC) while delivering RMSE close to the CRB across azimuth, range and velocity, particularly as resources (antennas, subcarriers, symbols) and SNR increase. These results demonstrate the practicality of high-resolution ISAC sensing in 6G-scale systems and highlight the method’s potential for real-time, multi-target scenarios, with future work on multipath and cooperative sensing.

Abstract

Integrated sensing and communication (ISAC) is a main application scenario of the sixth-generation mobile communication systems. Due to the fast-growing number of antennas and subcarriers in cellular systems, the computational complexity of joint azimuth-range-velocity estimation (JARVE) in ISAC systems is extremely high. This paper studies the JARVE problem for a monostatic ISAC system with orthogonal frequency division multiplexing (OFDM) waveform, in which a base station receives the echos of its transmitted cellular OFDM signals to sense multiple targets. The Cramer-Rao bounds are first derived for JARVE. A low-complexity algorithm is further designed for super-resolution JARVE, which utilizes the proposed iterative subspace update scheme and Levenberg-Marquardt optimization method to replace the exhaustive search of spatial spectrum in multiple-signal-classification (MUSIC) algorithm. Finally, with the practical parameters of 5G New Radio, simulation results verify that the proposed algorithm can reduce the computational complexity by three orders of magnitude and two orders of magnitude compared to the existing three-dimensional MUSIC algorithm and estimation-of-signal-parameters-using-rotational-invariance-techniques (ESPRIT) algorithm, respectively, and also improve the estimation performance.

Low-Complexity Joint Azimuth-Range-Velocity Estimation for Integrated Sensing and Communication with OFDM Waveform

TL;DR

This work addresses the high computational burden of joint azimuth-range-velocity estimation (JARVE) in OFDM-based ISAC systems. It derives the CRBs for JARVE and introduces a low-complexity PI-2DMUSIC approach that replaces exhaustive 3D MUSIC searches with spatial smoothing, a lightweight 3D parameter initialization, and iterative subspace updates using Levenberg–Marquardt refinement followed by MMSE-based 3D pairing. The proposed method achieves substantial complexity reductions (up to thousands of times faster than 3D-MUSIC) while delivering RMSE close to the CRB across azimuth, range and velocity, particularly as resources (antennas, subcarriers, symbols) and SNR increase. These results demonstrate the practicality of high-resolution ISAC sensing in 6G-scale systems and highlight the method’s potential for real-time, multi-target scenarios, with future work on multipath and cooperative sensing.

Abstract

Integrated sensing and communication (ISAC) is a main application scenario of the sixth-generation mobile communication systems. Due to the fast-growing number of antennas and subcarriers in cellular systems, the computational complexity of joint azimuth-range-velocity estimation (JARVE) in ISAC systems is extremely high. This paper studies the JARVE problem for a monostatic ISAC system with orthogonal frequency division multiplexing (OFDM) waveform, in which a base station receives the echos of its transmitted cellular OFDM signals to sense multiple targets. The Cramer-Rao bounds are first derived for JARVE. A low-complexity algorithm is further designed for super-resolution JARVE, which utilizes the proposed iterative subspace update scheme and Levenberg-Marquardt optimization method to replace the exhaustive search of spatial spectrum in multiple-signal-classification (MUSIC) algorithm. Finally, with the practical parameters of 5G New Radio, simulation results verify that the proposed algorithm can reduce the computational complexity by three orders of magnitude and two orders of magnitude compared to the existing three-dimensional MUSIC algorithm and estimation-of-signal-parameters-using-rotational-invariance-techniques (ESPRIT) algorithm, respectively, and also improve the estimation performance.
Paper Structure (18 sections, 69 equations, 6 figures, 2 tables, 3 algorithms)

This paper contains 18 sections, 69 equations, 6 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. System Description
  4. Signal Model
  5. Cramér-Rao Bound Performance Analysis
  6. Low-Complexity JARVE Algorithm Design
  7. Spatial Smoothing
  8. 3D-Parameter Initialization
  9. Iterative-subspace-updating 2D-MUSIC Parallel Estimation and 3D-Parameter Pairing
  10. Analyze for Computational Complexity
  11. Numerical Results
  12. Estimation Performance Comparison
  13. RMSE versus SNR
  14. RMSE versus Number of Antennas, Subcarriers and Symbols
  15. Computational Complexity Comparison
  16. ...and 3 more sections

Figures (6)

  • Figure 1: System Model for ISAC over OFDM waveform.
  • Figure 2: Receive Uniform Line Array Model.
  • Figure 3: Example of spatial smoothing for signal observations.
  • Figure 4: Comparison of JARVE RMSE versus SNR.
  • Figure 5: Comparison of JARVE RMSE versus Number of Antennas, Subcarriers and Symbols for SNR=5dB.
  • ...and 1 more figures

Theorems & Definitions (2)

  • proof
  • proof