Patch-Sinusoidally Modulated SSPPs Leaky-Wave Antenna and Its Random Forest-Assisted Optimization Design

TANG Luping; CHENG Yonghao; CHEN Yibo; LIAO Chen

doi:10.11999/JEIT260651

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TANG Luping, CHENG Yonghao, CHEN Yibo, LIAO Chen. Patch-Sinusoidally Modulated SSPPs Leaky-Wave Antenna and Its Random Forest-Assisted Optimization Design[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260651

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TANG Luping, CHENG Yonghao, CHEN Yibo, LIAO Chen. Patch-Sinusoidally Modulated SSPPs Leaky-Wave Antenna and Its Random Forest-Assisted Optimization Design[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260651

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Patch-Sinusoidally Modulated SSPPs Leaky-Wave Antenna and Its Random Forest-Assisted Optimization Design

doi: 10.11999/JEIT260651 cstr: 32379.14.JEIT260651

1.
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China
2.
School of Electronic Science and Engineering, Southeast University, Nanjing 210037, China
3.
School of Electronic and Optical Engineering, School of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210037, China

Funds: National Natural Science Foundation of China (Grant Nos. 52001168, 11704200), General Project of China Postdoctoral Science Foundation (Grant No. 2022M710668), Horizontal Project (Grant No. 028040426)

Accepted Date: 2026-08-26
Rev Recd Date: 2026-08-26

Available Online: 2026-09-01

Abstract

Abstract

Objective Spoof surface plasmon polaritons (SSPPs) leaky-wave antennas feature low-profile configuration and inherent frequency-scanning capability, making them promising for modern radar, communication, and intelligent sensing systems. However, strong nonlinear coupling among geometric parameters makes traditional optimization computationally costly, as full-wave simulations require thousands of evaluations and often converge to suboptimal local solutions due to landscape complexity. The leakage dynamics in SSPPs—slow-wave propagation, spatial harmonic coupling, and leakage rate distribution—adds complexity beyond conventional designs. To address this, we propose a patch-sinusoidally modulated SSPPs leaky-wave antenna and a machine learning framework integrating a random forest surrogate with particle swarm optimization (PSO) for efficient high-dimensional global optimization with reduced cost. Methods Unlike conventional groove-depth modulation, our antenna maintains uniform groove depth and a complete metal ground. Patch arrays with sinusoidally varying widths are loaded on both sides of the transmission line, with envelope functions $ Y=A\sin (Tx) $ and $ Y=A\sin (Tx+\pi ) $, enabling flexible leakage control. The antenna is fully described by a nine-dimensional continuous parameter vector: groove width g, depth s, period p, six transition lengths g₁–g₆, port dimensions l and w, and modulation A, T. Using Latin hypercube sampling, 450 parameter samples are generated to ensure uniform coverage. Full-wave frequency-domain simulations (COMSOL, 9 GHz, approximately 27 min each) extract gain, S₁₁, S₂₁, scanning angle, side lobe level (SLL), and total efficiency. Four regression models—MLP, SVR, random forest (RF), and GPR—are systematically trained. RF employs bootstrap resampling with hyperparameters optimized via random search cross-validation (trees: 500–1200, depth: 12–24, min samples per split: 2–5). The trained RF surrogate is then embedded into PSO (40 particles, 120 iterations, inertia 0.72, c₁=c₂=1.5) with a weighted fitness function (G:1.5, η:0.7, SLL:0.55, S₁₁:0.25, S₂₁:0.7, θ_scan:0.18). Results and Discussions RF achieves the highest average R² of 0.9554 across six outputs, outperforming GPR (0.9489), SVR (0.9212), and MLP (0.8952). For key radiation indicators, RF attains gain MAE of 0.032 dBi, SLL MAE of 0.168 dB, and efficiency MAE of 0.015. Scatter plots of predicted versus simulated values cluster tightly around the diagonal, and residual histograms show means near zero with no systematic bias, confirming excellent prediction accuracy and generalization. After RF-PSO optimization, full-wave simulation confirms substantial improvements: gain rises from 13.84 dBi to 14.52 dBi, SLL drops from –17 dB to –19 dB,peak total efficiency increases from 84.9% to 92.9%, S₁₁ improves from –23.26 dB to –27.92 dB (4.66 dB), and scanning range expands from 57.2° to 61.5°. The scanning angle versus frequency curve exhibits good linearity across the operating band, and the two-dimensional far-field patterns show improved symmetry. The decrease in S₂₁ (from –3.76 dB to –5.50 dB) together with the gain/efficiency increase indicates that more energy is effectively converted into radiation rather than being dissipated or reflected. Sensitivity analysis with ±2% perturbations (50 samples) shows all coefficients of variation (CV) below 1.3%: gain CV 0.21% (<±0.1 dBi), SLL CV 1.26% (±0.4 dB), efficiency CV 0.88% (±0.01), S₁₁ CV 0.97%, S₂₁ CV 0.91%, scan CV 0.37%. These fluctuations are far smaller than optimization gains, confirming excellent robustness under typical fabrication tolerances. Comparison with recent leaky-wave antennas (both SSPP-based and SIW) demonstrates superior SLL (−19 dB), competitive efficiency (89% vs. 94.95% and90% in prior SSPP work), and scanning range (61.5°) outperforming most single-port SSPP antennas (e.g., 20°, 16°, 33°, 13°). The number of full-wave simulations is reduced by approximately 90% (450 training + 1 validation vs. 4,800 simulations for conventional PSO). Conclusions This paper proposes a patch-sinusoidally modulated SSPPs leaky-wave antenna and an RF-assisted PSO framework for synergistic optimization in nine dimensions. The RF surrogate achieves an average R² of 0.9554. The optimized antenna shows significantly improved performance across all metrics: gain by 0.68 dBi, SLL by 2 dB, efficiency by 8%, S₁₁ by 4.66 dB, and scanning range by 4.29°, while maintaining compact dimensions. Sensitivity analysis confirms robustness under typical fabrication tolerances. The proposed methodology reduces the number of full-wave simulations by approximately 90%. This work marks a methodological advancement by introducing machine learning surrogate modeling into SSPPs leaky-wave antenna design for efficient high-dimensional optimization. Future work includes fabrication, experimental validation, extension to millimeter-wave bands, and reconfigurable antenna designs.
- Spoof surface plasmon polaritons,
- Leaky-wave antenna,
- Sinusoidal modulation,
- Random forest,
- Particle swarm optimization

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References(20)

References

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