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Multi-Objective Optimization-Based Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems

Peng Wang, Dongsheng Han, Yashuai Cao, Wanli Ni, Dusit Niyato

TL;DR

This paper addresses waveform design for downlink multi-user and multi-target MIMO-ISAC systems by formulating a MOOP that balances communication sum-rate and radar sensing accuracy under CI-based precoding to mitigate MUI. A weighted Tchebycheff transformation creates a Pareto front from two subproblems: CI-precoded communication maximizing $R_{ ext{sum}}$ and beampattern-based sensing minimizing the MSE $M_s(\eta,\mathbf{R})$, with solution via GP, SDR, and SCA techniques. The proposed framework yields a Pareto front that enables flexible waveform design under different C&S preferences, with simulations highlighting the impact of transmit power and antenna count on the trade-off and beam patterns. Overall, the approach offers a practical, theory-grounded method to tailor ISAC waveforms for complex multi-user/multi-target environments, facilitating 6G ISAC deployments.

Abstract

Integrated sensing and communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems, where both communication and sensing (C&S) functionalities co-exist by sharing the same hardware platform and radio resource. In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences. The multi-user interference (MUI) may critically degrade the communication performance. To eliminate the MUI, we employ the constructive interference mechanism into the ISAC system, which saves the power budget for communication. However, due to the conflict between C&S metrics, it is intractable for the ISAC system to achieve the optimal performance of C&S objective simultaneously. Therefore, it is important to strike a trade-off between C&S objectives. By virtue of the multi-objective optimization theory, we propose a weighted Tchebycheff-based transformation method to re-frame the C&S trade-off problem as a Pareto-optimal problem, thus effectively tackling the constraints in ISAC systems. Finally, simulation results reveal the trade-off relation between C&S performances, which provides insights for the flexible waveform design under different C&S performance preferences in MIMO-ISAC systems.

Multi-Objective Optimization-Based Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems

TL;DR

This paper addresses waveform design for downlink multi-user and multi-target MIMO-ISAC systems by formulating a MOOP that balances communication sum-rate and radar sensing accuracy under CI-based precoding to mitigate MUI. A weighted Tchebycheff transformation creates a Pareto front from two subproblems: CI-precoded communication maximizing and beampattern-based sensing minimizing the MSE , with solution via GP, SDR, and SCA techniques. The proposed framework yields a Pareto front that enables flexible waveform design under different C&S preferences, with simulations highlighting the impact of transmit power and antenna count on the trade-off and beam patterns. Overall, the approach offers a practical, theory-grounded method to tailor ISAC waveforms for complex multi-user/multi-target environments, facilitating 6G ISAC deployments.

Abstract

Integrated sensing and communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems, where both communication and sensing (C&S) functionalities co-exist by sharing the same hardware platform and radio resource. In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences. The multi-user interference (MUI) may critically degrade the communication performance. To eliminate the MUI, we employ the constructive interference mechanism into the ISAC system, which saves the power budget for communication. However, due to the conflict between C&S metrics, it is intractable for the ISAC system to achieve the optimal performance of C&S objective simultaneously. Therefore, it is important to strike a trade-off between C&S objectives. By virtue of the multi-objective optimization theory, we propose a weighted Tchebycheff-based transformation method to re-frame the C&S trade-off problem as a Pareto-optimal problem, thus effectively tackling the constraints in ISAC systems. Finally, simulation results reveal the trade-off relation between C&S performances, which provides insights for the flexible waveform design under different C&S performance preferences in MIMO-ISAC systems.
Paper Structure (20 sections, 2 theorems, 45 equations, 11 figures, 1 table, 3 algorithms)

This paper contains 20 sections, 2 theorems, 45 equations, 11 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Motivations and Challenges
  4. Contributions and Organization
  5. System Model
  6. Communication Model
  7. Radar Sensing Model
  8. Problem Formulation
  9. Precoding Design with CI
  10. MIMO Radar Waveform Design
  11. Multi-Objective Optimization for ISAC
  12. Proposed Optimization Algorithm
  13. Trade-off of Dual-Fold Performance
  14. Optimization of Communication Sum Rate
  15. Optimization of Sensing Error
  16. ...and 5 more sections

Key Result

Lemma 1

If we define GP problem as where objective function casa and constraints casb are monotonic monomial, then the GP problem can be transformed into a convex optimization problem through variable substitution and function transformation.

Figures (11)

  • Figure 1: CI-based multi-user and multi-target ISAC framework.
  • Figure 2: Convergence behavior of Algorithm 3.
  • Figure 3: Trade-off between sum rate and sensing MSE achieved by different schemes.
  • Figure 4: Performance gain of communication and sensing versus (a) sum power, and (b) number of transmit antennas
  • Figure 5: Trade-off between sum rate and sensing MSE under different power budget.
  • ...and 6 more figures

Theorems & Definitions (3)

  • Definition 1
  • Lemma 1
  • Proposition 1