Constant-Modulus Waveform Design for SLP-Based DFRC Systems

ZHAI Yihao; ZHAO Hongyu; NIU Fengliang; LI Jingyan

doi:10.11999/JEIT250288

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ZHAI Yihao, ZHAO Hongyu, NIU Fengliang, LI Jingyan. Constant-Modulus Waveform Design for SLP-Based DFRC Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250288

Citation:

ZHAI Yihao, ZHAO Hongyu, NIU Fengliang, LI Jingyan. Constant-Modulus Waveform Design for SLP-Based DFRC Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250288

ZHAI Yihao, ZHAO Hongyu, NIU Fengliang, LI Jingyan. Constant-Modulus Waveform Design for SLP-Based DFRC Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250288

Citation:

ZHAI Yihao, ZHAO Hongyu, NIU Fengliang, LI Jingyan. Constant-Modulus Waveform Design for SLP-Based DFRC Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250288

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Constant-Modulus Waveform Design for SLP-Based DFRC Systems

doi: 10.11999/JEIT250288 cstr: 32379.14.JEIT250288

Unit 63892 of PLA, Luoyang 471032, China

Received Date: 2025-04-17
Rev Recd Date: 2025-09-15

Available Online: 2025-09-22

Abstract

Abstract

Objective Dual-Function Radar–Communication (DFRC) Integration is a promising technology for addressing spectrum congestion. The key challenge in DFRC lies in designing a dual-functional waveform that simultaneously supports both radar sensing and communication. Previous research has primarily focused on designing transmit waveforms under Multi-User Interference (MUI) energy constraints. However, not all MUI energy is detrimental in DFRC systems. Symbol-Level Precoding (SLP) can exploit symbol information to transform harmful MUI into constructive components, thereby enhancing communication Quality of Service (QoS). To further leverage the advantages of SLP in mitigating MUI energy and to improve target detection in cluttered environments, this work studies the joint design of transmit waveforms and receive filters for DFRC systems. Methods The analytical expression of the radar output Signal-to-Interference-plus-Noise Ratio (SINR) is first derived. To guarantee communication performance, Constructive Interference (CI) constraints are formulated using SLP. Based on these, a joint optimization problem is established, where maximizing the radar output SINR serves as the objective function and the communication CI constraints are imposed. A constant-modulus condition is also enforced to prevent nonlinear distortion of the transmitted waveform. To address the non-convex nature of the joint optimization problem, an iterative algorithm based on cyclic optimization is proposed. For a fixed transmitted waveform, the subproblem reduces to a Minimum Variance Distortionless Response (MVDR) problem. For a fixed filter, the communication CI constraints are incorporated into the objective function of the subproblem as a penalty term, leading to an unconstrained problem whose feasible region is a Riemannian complex manifold. This problem is then solved efficiently using the Riemannian Conjugate Gradient (RCG) method. Results and Discussions As shown in (Fig 3): (a) the proposed method converges within a short time, and (b) the designed waveform satisfies the constant-modulus constraint. (Figs. 4 and 5) analyze the effect of the number of communication users and the communication QoS threshold on radar output SINR and communication SER, confirming the trade-off between radar sensing and communication performance. (Figs. 6 and 7) illustrate the effect of interference power and communication QoS threshold on radar output SINR and communication SER, demonstrating the robustness of the proposed method under different interference power levels. When communication QoS requirements are fixed, the radar output SINR and communication SER performance of the three compared cases remain similar. (Fig. 8(a)) shows the radar output SINR versus the number of communication users. The results indicate that the proposed CI-based waveform consistently outperforms the conventional MUI-based waveform in radar output SINR. Moreover, the two CI-based methods achieve almost identical SINR performance. (Fig. 8(b)) presents the radar received beampatterns, where it can be observed that the deep nulls formed in the interference directions are deeper than those obtained with the conventional MUI-based method. This confirms that the proposed method effectively suppresses signal-dependent interference. (Fig. 9(a)) presents the received communication constellations. The synthesized communication signals of the CI-based methods are more sparsely distributed and exhibit higher power compared with the conventional MUI-based method. (Fig. 9(b)) further evaluates the communication SER performance, showing that the proposed method achieves a lower SER. This improvement arises because the communication CI constraints transform harmful MUI into useful signal energy, thereby enhancing communication QoS. (Fig. 10) presents the relationship between CPU time and the number of communication users. The results show that CPU time increases for all methods as the number of communication users grows, since a larger number of users reduces the feasibility region. Nevertheless, the proposed method demonstrates significantly higher computational efficiency compared with existing approaches. Conclusions This paper investigates the joint design of transmit waveforms and receive filters for MIMO-DFRC systems. A joint optimization problem is formulated to maximize radar output SINR under constant-modulus and communication CI constraints. To solve the resulting non-convex problem, an alternating optimization algorithm based on the RCG method is developed. Simulation results demonstrate that CI-based methods outperform the MUI-based ADMM method in both radar sensing performance and communication QoS. In addition, the proposed method achieves faster convergence compared with the MM-neADMM method, with only a minor loss in radar sensing performance.
- Dual-Functional Radar–Communication (DFRC),
- Symbol-Level Precoding (SLP),
- Constructive Interference (CI),
- Manifold optimization

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References(24)

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