ML-Augmented Optimization of LoRa Antennas for Drone Telemetry
A compact printed monopole antenna for drone telemetry communication, operating at 433 MHz, with a gain of 2.2 dBi, is designed. The antenna is fabricated on an FR-4 substrate with dimensions of <inline-formula> <tex-math notation="LaTeX">$0.116~\lambda _{0} \times 0.073~\lambd...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11091312/ |
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| author | Pothala Chaya Devi Ramarakula Madhu |
| author_facet | Pothala Chaya Devi Ramarakula Madhu |
| author_sort | Pothala Chaya Devi |
| collection | DOAJ |
| description | A compact printed monopole antenna for drone telemetry communication, operating at 433 MHz, with a gain of 2.2 dBi, is designed. The antenna is fabricated on an FR-4 substrate with dimensions of <inline-formula> <tex-math notation="LaTeX">$0.116~\lambda _{0} \times 0.073~\lambda _{0}$ </tex-math></inline-formula> and is optimized for long-range communication. A Machine Learning-augmented Optimization (MLaO) method is proposed to reduce the antenna design time compared to traditional electromagnetic simulation techniques. Typically, antenna design involves complex computer simulations like CST and computationally intensive parameter sweeps. However, in this work a surrogate Artificial Neural Network (ANN) model trained on 1080 antenna designs replaces the heavy CST simulations. This ANN model is then coupled with a Simulated Annealing (SA) optimizer to generate antennas with the desired characteristics, reducing the total design time to 57% compared to traditional techniques. Three antenna designs were simulated using MLaO for different long-range (LoRa) frequency bands, with Ata1 (433 MHz) achieved a return loss (S11) of −23.3 dB, Ata2 (865 MHz) had an S11 of −30.6 dB, and Ata3 (dual band at 433 MHz and 865 MHz) with S11 of −15.6 dB and −35.4 dB respectively. The fabricated antenna (433 MHz) was mounted on a drone and tested with a 3DR-433 telemetry transceiver, recording an average Received Signal Strength Indicator (RSSI) of −57.8 dBm up to 470 m. These results demonstrate the proposed antenna’s efficiency, compactness, and the effectiveness of the MLaO approach for fast and accurate antenna design. |
| format | Article |
| id | doaj-art-49fd5b41cfc94266b52fd7fc55295033 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-49fd5b41cfc94266b52fd7fc552950332025-08-20T03:31:30ZengIEEEIEEE Access2169-35362025-01-011313035313036210.1109/ACCESS.2025.359209611091312ML-Augmented Optimization of LoRa Antennas for Drone TelemetryPothala Chaya Devi0https://orcid.org/0000-0003-3275-6745Ramarakula Madhu1Department of ECE, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, Andhra Pradesh, IndiaDepartment of ECE, Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh, IndiaA compact printed monopole antenna for drone telemetry communication, operating at 433 MHz, with a gain of 2.2 dBi, is designed. The antenna is fabricated on an FR-4 substrate with dimensions of <inline-formula> <tex-math notation="LaTeX">$0.116~\lambda _{0} \times 0.073~\lambda _{0}$ </tex-math></inline-formula> and is optimized for long-range communication. A Machine Learning-augmented Optimization (MLaO) method is proposed to reduce the antenna design time compared to traditional electromagnetic simulation techniques. Typically, antenna design involves complex computer simulations like CST and computationally intensive parameter sweeps. However, in this work a surrogate Artificial Neural Network (ANN) model trained on 1080 antenna designs replaces the heavy CST simulations. This ANN model is then coupled with a Simulated Annealing (SA) optimizer to generate antennas with the desired characteristics, reducing the total design time to 57% compared to traditional techniques. Three antenna designs were simulated using MLaO for different long-range (LoRa) frequency bands, with Ata1 (433 MHz) achieved a return loss (S11) of −23.3 dB, Ata2 (865 MHz) had an S11 of −30.6 dB, and Ata3 (dual band at 433 MHz and 865 MHz) with S11 of −15.6 dB and −35.4 dB respectively. The fabricated antenna (433 MHz) was mounted on a drone and tested with a 3DR-433 telemetry transceiver, recording an average Received Signal Strength Indicator (RSSI) of −57.8 dBm up to 470 m. These results demonstrate the proposed antenna’s efficiency, compactness, and the effectiveness of the MLaO approach for fast and accurate antenna design.https://ieeexplore.ieee.org/document/11091312/Design timedroneFR-4LoRaMLaOsurrogate ANN |
| spellingShingle | Pothala Chaya Devi Ramarakula Madhu ML-Augmented Optimization of LoRa Antennas for Drone Telemetry IEEE Access Design time drone FR-4 LoRa MLaO surrogate ANN |
| title | ML-Augmented Optimization of LoRa Antennas for Drone Telemetry |
| title_full | ML-Augmented Optimization of LoRa Antennas for Drone Telemetry |
| title_fullStr | ML-Augmented Optimization of LoRa Antennas for Drone Telemetry |
| title_full_unstemmed | ML-Augmented Optimization of LoRa Antennas for Drone Telemetry |
| title_short | ML-Augmented Optimization of LoRa Antennas for Drone Telemetry |
| title_sort | ml augmented optimization of lora antennas for drone telemetry |
| topic | Design time drone FR-4 LoRa MLaO surrogate ANN |
| url | https://ieeexplore.ieee.org/document/11091312/ |
| work_keys_str_mv | AT pothalachayadevi mlaugmentedoptimizationofloraantennasfordronetelemetry AT ramarakulamadhu mlaugmentedoptimizationofloraantennasfordronetelemetry |