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Hardware Distortion Aware Precoding for ISAC Systems

Murat Babek Salman, Emil Björnson, Özlem Tugfe Demir

TL;DR

The paper addresses hardware impairments in ISAC systems operating in cluttered environments and their effect on sensing performance. It derives a closed-form sensing SCNR that accounts for both transmit and receive distortions and clutter, and proposes distortion- and clutter-aware precoding along with a low-complexity power-allocation method. The authors formulate and solve two optimization pathways: a distortion-aware MMSE-based precoder with iterative convexification (P2) and a convex QP for power allocation (P3). Numerical results show that the proposed designs restore sensing performance and preserve communication SE, with gains increasing with more antennas and lower impairment levels.

Abstract

The impact of hardware impairments on the spectral efficiency of communication systems is well studied, but their effect on sensing performance remains unexplored. In this paper, we analyze the influence of hardware impairments on integrated sensing and communication (ISAC) systems in cluttered environments. We derive the sensing signal-to-clutter-plus-noise ratio (SCNR) and show that hardware distortions significantly degrade sensing performance by enhancing clutter-induced noise, which masks target echoes. The isotropic nature of transmit distortion due to multiple stream transmission further complicates clutter suppression. To address this, we propose a distortion- and clutter-aware precoding strategy that minimizes the deviation from the communication-optimized precoder while improving sensing robustness. We also propose an alternative power allocation-based approach that reduces computational complexity. Numerical results confirm the effectiveness of the proposed approaches in overcoming hardware- and clutter-induced limitations, demonstrating significant performance gains over distortion-unaware designs.

Hardware Distortion Aware Precoding for ISAC Systems

TL;DR

The paper addresses hardware impairments in ISAC systems operating in cluttered environments and their effect on sensing performance. It derives a closed-form sensing SCNR that accounts for both transmit and receive distortions and clutter, and proposes distortion- and clutter-aware precoding along with a low-complexity power-allocation method. The authors formulate and solve two optimization pathways: a distortion-aware MMSE-based precoder with iterative convexification (P2) and a convex QP for power allocation (P3). Numerical results show that the proposed designs restore sensing performance and preserve communication SE, with gains increasing with more antennas and lower impairment levels.

Abstract

The impact of hardware impairments on the spectral efficiency of communication systems is well studied, but their effect on sensing performance remains unexplored. In this paper, we analyze the influence of hardware impairments on integrated sensing and communication (ISAC) systems in cluttered environments. We derive the sensing signal-to-clutter-plus-noise ratio (SCNR) and show that hardware distortions significantly degrade sensing performance by enhancing clutter-induced noise, which masks target echoes. The isotropic nature of transmit distortion due to multiple stream transmission further complicates clutter suppression. To address this, we propose a distortion- and clutter-aware precoding strategy that minimizes the deviation from the communication-optimized precoder while improving sensing robustness. We also propose an alternative power allocation-based approach that reduces computational complexity. Numerical results confirm the effectiveness of the proposed approaches in overcoming hardware- and clutter-induced limitations, demonstrating significant performance gains over distortion-unaware designs.

Paper Structure

This paper contains 11 sections, 22 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System Model
  3. Transmitted Signal Model
  4. Received Communication Signal
  5. Received Echo Signal
  6. Performance Analysis and Precoder Design
  7. Sensing SCNR Derivation
  8. Direct Precoder Design
  9. Power Allocation-Based Precoder Design
  10. Performance Evaluations
  11. Conclusion

Figures (4)

  • Figure 1: CDF of sensing SCNR (solid lines: precoding, dashed lines: power allocation).
  • Figure 2: Average sum SE vs. number of antennas $M$ (solid lines: ideal hardware, dashed lines: imperfect hardware).
  • Figure 3: Average sum SE vs. transmit hardware distortion factor $\kappa_{\text{t}}$ (dashed lines: ideal hardware, solid lines: imperfect hardware).
  • Figure 4: Average sum SE vs. receive hardware distortion factor $\kappa_{\text{r}}$ (dashed lines: ideal hardware, solid lines: imperfect hardware).

Theorems & Definitions (1)

  • Remark 1