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Sparse Orthogonal Matching Pursuit-based Parameter Estimation for Integrated Sensing and Communications

Ngoc-Son Duong, Khac-Hoang Ngo, Thai-Mai Dinh, Van-Linh Nguyen

TL;DR

This work tackles parameter estimation in mmWave ISAC systems by exploiting a shared AOA between sensing and uplink channels. It introduces an $OMP$-based coarse AOA estimator that leverages a Dirichlet-structured beamspace, followed by a refined joint estimation using a modified $SAGE$ algorithm to update sensing and communication parameters. Results show that sharing the AOA reduces the CRLB and improves AOA RMSE, particularly at low SNR, with the joint approach outperforming standalone sensing or communication. The framework promises reduced pilot overhead and enhanced estimation accuracy for integrated sensing and communications in dynamic mmWave scenarios.

Abstract

Accurate parameter estimation such as angle of arrival (AOA) is essential to enhance the performance of integrated sensing and communication (ISAC) in mmWave multiple-input multiple-output (MIMO) systems. This work presents a sensing-aided communication channel estimation mechanism, where the sensing channel shares the same AOA with the uplink communication channel. First, we propose a novel orthogonal matching pursuit (OMP)-based method for coarsely estimating the AOA in a sensing channel, offering improved accuracy compared to conventional methods that rely on rotational invariance techniques. Next, we refine the coarse estimates obtained in the first step by modifying the Space-Alternating Generalized Expectation Maximization algorithm for fine parameter estimation. Through simulations and mathematical analysis, we demonstrate that scenarios with shared AOA achieve a better Cramer-Rao lower bound (CRLB) than those without sharing. This finding highlights the potential of leveraging joint sensing and communication channels to enhance parameter estimation accuracy, particularly in channel or location estimation applications.

Sparse Orthogonal Matching Pursuit-based Parameter Estimation for Integrated Sensing and Communications

TL;DR

This work tackles parameter estimation in mmWave ISAC systems by exploiting a shared AOA between sensing and uplink channels. It introduces an -based coarse AOA estimator that leverages a Dirichlet-structured beamspace, followed by a refined joint estimation using a modified algorithm to update sensing and communication parameters. Results show that sharing the AOA reduces the CRLB and improves AOA RMSE, particularly at low SNR, with the joint approach outperforming standalone sensing or communication. The framework promises reduced pilot overhead and enhanced estimation accuracy for integrated sensing and communications in dynamic mmWave scenarios.

Abstract

Accurate parameter estimation such as angle of arrival (AOA) is essential to enhance the performance of integrated sensing and communication (ISAC) in mmWave multiple-input multiple-output (MIMO) systems. This work presents a sensing-aided communication channel estimation mechanism, where the sensing channel shares the same AOA with the uplink communication channel. First, we propose a novel orthogonal matching pursuit (OMP)-based method for coarsely estimating the AOA in a sensing channel, offering improved accuracy compared to conventional methods that rely on rotational invariance techniques. Next, we refine the coarse estimates obtained in the first step by modifying the Space-Alternating Generalized Expectation Maximization algorithm for fine parameter estimation. Through simulations and mathematical analysis, we demonstrate that scenarios with shared AOA achieve a better Cramer-Rao lower bound (CRLB) than those without sharing. This finding highlights the potential of leveraging joint sensing and communication channels to enhance parameter estimation accuracy, particularly in channel or location estimation applications.

Paper Structure

This paper contains 11 sections, 1 theorem, 33 equations, 4 figures, 2 algorithms.

Table of Contents

  1. Introduction
  2. System and Channel Model
  3. Sensing Channel
  4. Communication Channel
  5. Proposed sensing-aided parameter estimation for mmWave MIMO ISAC system
  6. Proposed OMP Algorithm for Sensing Function
  7. Proposed strategy for refinement procedure
  8. Simulation results and discussion
  9. Coarse Estimation Performance
  10. Fine Estimation Performance
  11. Conclusion

Key Result

Corollary 1

Considering a system consisting of two subsystems, for example, an uplink communication system $S_1$ and a sensing system $S_2$. If the subsystems share a common parameter $\bm{\theta}$, then the CRLB of shared $\bm{\theta}$ is lower than elemental CRLBs of $\bm{\theta}$ assuming they operate indepe

Figures (4)

  • Figure 1: System model of an mmWave MIMO ISAC system.
  • Figure 2: Successful recovery probability versus SNR (dB) in the coarse estimation step
  • Figure 3: RMSE of AOA versus SNR (dB) in the coarse estimation step.
  • Figure 4: RMSE of AOA vs. SNR (dB) in the refinement step

Theorems & Definitions (1)

  • Corollary 1