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Machine learning inspired compact quad port MIMO antenna with circular polarization for multi band wireless
Ashish Kumar1, Aarti Bansal2, Gurprince Singh2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Scientific Reports
|July 1, 2026
Summary
This study introduces a compact quad-element MIMO antenna for C, X, and Ku bands, achieving circular polarization and high isolation. Optimized with machine learning, it offers excellent diversity performance for wireless applications.
Area of Science:
- Antenna Engineering
- Wireless Communications
- Electromagnetics
Background:
- Modern wireless systems require compact, multi-band antennas with high performance.
- Circular Polarization (CP) is crucial for mitigating multipath fading and polarization mismatch.
- Achieving high inter-element isolation in multi-element antenna systems is a significant challenge.
Purpose of the Study:
- To design and validate a compact quad-element MIMO antenna operating across C, X, and Ku bands.
- To achieve geometry-driven Circular Polarization (CP) without external components.
- To ensure high inter-element isolation and superior diversity performance for advanced wireless applications.
Main Methods:
- A Y-shaped radiating monopole configuration with connected ground elements was employed.
- Machine learning algorithms, specifically Gaussian regression, were used for parameter optimization.
- Prototype fabrication and experimental testing were conducted to validate simulation results.
Main Results:
- The antenna resonates at 6.39 GHz, 11.09 GHz, 14.69 GHz, and 17.96 GHz.
- Geometry-driven CP was achieved, and inter-element isolation (Sij) remained below -20 dB.
- Excellent diversity performance metrics were obtained: ECC (≤0.001), DG (≈10 dB), CCL (<0.2 bps/Hz), TARC (>9.99), and ergodic channel capacity (>9 bps/Hz).
Conclusions:
- The developed quad-element MIMO antenna is a compact, high-performance solution for CP-MIMO systems.
- Its robust performance, including CP and high isolation, makes it suitable for satellite and advanced wireless communication.
- The integration of machine learning for optimization enhances antenna design efficiency and performance.
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