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Robust Beamforming Design for Coherent Distributed ISAC with Statistical RCS and Phase Synchronization Uncertainty

Seonghoon Yoo, Seulhyun Kwon, Kawon Han, Elaheh Ataeebojd, Mehdi Rasti, Joonhyuk Kang

Abstract

Distributed integrated sensing and communication (D-ISAC) enables multiple spatially distributed nodes to cooperatively perform sensing and communication. However, achieving coherent cooperation across distributed nodes is challenging due to practical impairments. In particular, residual phase synchronization errors result in imperfect channel state information (CSI), while angle-of-arrival (AoA) uncertainties induce radar cross-section (RCS) variations. These impairments jointly degrade target detection performance in D-ISAC systems. To address these challenges jointly, this paper proposes a robust beamforming design for coherent D-ISAC systems. Multiple distributed nodes coordinated by a central unit (CU) jointly perform joint transmission coordinated multipoint (JT-CoMP) communication and multi-input multi-output (MIMO) radar sensing to detect a target while serving multiple user equipments (UEs). We formulate a robust beamforming problem that maximizes the expected Kullback-Leibler divergence (KLD) under statistical RCS variations while satisfying system power and per-user minimum signal-to-interference-plus-noise ratio (SINR) constraints under imperfect CSI to ensure the communication quality of service (QoS). The problem is solved using semidefinite relaxation (SDR) and successive convex approximation (SCA), and numerical results show that the proposed method achieves up to 3 dB signal-to-clutter-plus-noise ratio (SCNR) gain over the conventional beamforming schemes for target detection while maintaining the required communication QoS.

Robust Beamforming Design for Coherent Distributed ISAC with Statistical RCS and Phase Synchronization Uncertainty

Abstract

Distributed integrated sensing and communication (D-ISAC) enables multiple spatially distributed nodes to cooperatively perform sensing and communication. However, achieving coherent cooperation across distributed nodes is challenging due to practical impairments. In particular, residual phase synchronization errors result in imperfect channel state information (CSI), while angle-of-arrival (AoA) uncertainties induce radar cross-section (RCS) variations. These impairments jointly degrade target detection performance in D-ISAC systems. To address these challenges jointly, this paper proposes a robust beamforming design for coherent D-ISAC systems. Multiple distributed nodes coordinated by a central unit (CU) jointly perform joint transmission coordinated multipoint (JT-CoMP) communication and multi-input multi-output (MIMO) radar sensing to detect a target while serving multiple user equipments (UEs). We formulate a robust beamforming problem that maximizes the expected Kullback-Leibler divergence (KLD) under statistical RCS variations while satisfying system power and per-user minimum signal-to-interference-plus-noise ratio (SINR) constraints under imperfect CSI to ensure the communication quality of service (QoS). The problem is solved using semidefinite relaxation (SDR) and successive convex approximation (SCA), and numerical results show that the proposed method achieves up to 3 dB signal-to-clutter-plus-noise ratio (SCNR) gain over the conventional beamforming schemes for target detection while maintaining the required communication QoS.

Paper Structure

This paper contains 24 sections, 1 theorem, 46 equations, 8 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Coherent D-ISAC System
  4. Communication System Model
  5. Sensing System Model
  6. Imperfect Target AoA and Statistical RCS Model
  7. Distributed Sensing Channel and Detection Model
  8. KLD Derivation for Centralized Target Detection Framework
  9. Proposed Robust D-ISAC Beamforming
  10. Problem Formulation
  11. Proposed Robust D-ISAC Beamforming
  12. Phase Synchronization Error in Communication
  13. KLD Characterization under Statistical RCS Uncertainty
  14. Imperfect Target AoA Information
  15. SCA-Based KLD Relaxation
  16. ...and 9 more sections

Key Result

Lemma 1

For a given CSI uncertainty set $\mathcal{E}_{n,k}=\{\mathbf{e}_{n,k}:\lVert \mathbf{e}_{n,k} \rVert^2\leq \delta^2\}$, the worst-case desired signal power and interference power in min_max_SINR are given by signal_min and interference_max at the top of this page, respectively. The minimum desired s

Figures (8)

  • Figure 1: System model of the considered robust coherent D-ISAC network. Multiple spatially distributed ISAC nodes collaboratively transmit superposed communication and sensing waveforms and forward the received echoes to a CU for centralized processing. Statistical RCS fluctuations and phase-synchronization errors are explicitly considered in the robust beamforming design.
  • Figure 2: Illustrative polar RCS patterns under different statistical RCS models for a target located at the center of the network: (a) Chi-square model and (b) Swerling I model.
  • Figure 3: Convergence of the proposed robust beamforming algorithm with $K=3$, $\Gamma_c=20$ dB.
  • Figure 4: KLD performance of the proposed robust beamforming versus SINR threshold $\Gamma_c$ in a D-ISAC system with $K=3$, and $\Delta\theta_{s,n}=2^{\circ}$.
  • Figure 5: Detection probability versus input SCNR under different SINR thresholds $\Gamma_c$, with $K=3$, $N=2$, $M=12$, and $\Delta\theta_{s,n}=2^{\circ}$.
  • ...and 3 more figures

Theorems & Definitions (2)

  • Lemma 1
  • proof