Yahya, M.S. and Soeung, S. and Rahim, S.K.A. and Musa, U. and Ba Hashwan, S.S. and Haque, M.A. (2024) Machine Learning-Optimized Compact Frequency Reconfigurable Antenna With RSSI Enhancement for Long-Range Applications. IEEE Access, 12. pp. 10970-10987. ISSN 21693536
Full text not available from this repository.Abstract
This study presents an innovative and compact monopole antenna with dual-band frequency reconfigurability for LoRa applications. It operates within the 915 MHz and 868 MHz frequencies, aligning with the designated bands for use in America, Asia and Europe. No existing compact reconfigurable antenna with these features for LoRa applications within ISM bands below 1 GHz is known. Employing an economical FR-4 substrate in its design, the antenna attains a compact size of 40 � 42 mm2 (0.12 λ 0 � 0.12 λ 0), where λ 0 denotes the wavelength in free space corresponding to 868 MHz. A single RF PIN diode enables seamless switching between 868 MHz and 915 MHz bands. Design, simulation, and optimization employed CST MWS® software. Supervised regression Machine Learning (ML) models predicted resonance frequencies, with Gaussian Process Regression emerging as optimal, achieving R-squared and variance scores of 92.87 and 93.77, respectively. A maximum gain of 2 dBi at 915 MHz and 70 efficiency, boasting good radiation patterns and matching was demonstrated by the antenna. Experimental validation in a football field at Universiti Teknologi PETRONAS, Malaysia, assessed the proposed antenna's performance on a LoRa transceiver system based on LoRa SX1276. The Received Signal Strength Indicator (RSSI) of the proposed antenna consistently exceeded the conventional commercially available monopole antenna by an average of -12 dBm at every point up to 300 m, showcasing enhanced signal reception. The antenna proves promising for wireless sensor nodes in long-range applications. © 2013 IEEE.
Item Type: | Article |
---|---|
Additional Information: | cited By 0 |
Uncontrolled Keywords: | Computer software; Directional patterns (antenna); Microwave antennas; Monopole antennas; Radiation efficiency; Radio transceivers; Semiconductor diodes; Sensor nodes; Smart antennas; Substrates, Adaptive arrays; Antennas measurement; Dual Band; Gain; Lora; Machine-learning; PIN-diode; Received signal strength indicators; Reconfigurable, Natural frequencies |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 04 Jun 2024 14:19 |
Last Modified: | 04 Jun 2024 14:19 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/20224 |