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2024-12-13
PIER Letters
Vol. 124, 9-16, 2025
download: 97
Performance Enhancement of Substrate Integrated Waveguide Antenna for Wi-Fi Applications
Srisudharshan Manikandan, Anbazhagan Vidya Linkkesh, Shankaragouda M. Patil and Venkatesan Rajeshkumar
A single-band, linearly polarized Substrate Integrated Waveguide (SIW) antenna is designed specifically for WLAN 802.11a applications. The SIW design consists of four rectangular slots adjacent to each other through the SIW wall, with appropriate rectangular patch elements inserted in the two vertical slots for bandwidth enhancement. The structure is optimized to radiate at a frequency of 5.22 GHz, resulting in linear polarization caused by the excitation of the TE110 mode. The simulated design offers a gain of 7.275 dBi and a bandwidth of 47 MHz. The radiation pattern of the proposed fabricated antenna is measured in test environments where it is found to be unidirectional. The proposed design is compact and minimal in complexity, offering a higher gain.
Performance Enhancement of Substrate Integrated Waveguide Antenna for Wi-Fi Applications
2024-12-06
PIER Letters
Vol. 124, 1-7, 2025
download: 84
Application of Machine Learning in Urban Base Station Placement for 5G Communications and Beyond
Irfan Farhan Mohamad Rafie, Soo Yong Lim and Michael Jenn Hwan Chung
Optimal placement of wireless base stations in urban areas allows for maximum coverage and performance whilst maintaining minimal cost. In this paper, we propose a novel machine learning approach to place base stations rapidly in an urban environment for 5G communications and beyond. This is a noteworthy approach as 5G, especially those that involve millimeter wave frequencies tend to require significantly higher number of base stations for any particular area, unlike their counterpart low frequencies where a small number of base station is sufficient to cover a good geographical area. Our machine learning empowered path loss model is developed to tackle this change in gameplay head-on, and it bridges the gap between empirical and ray tracing methods where we achieve accuracy closer to ray tracing yet at a significantly lower computation cost. Promising preliminary results are obtained, with a minimum coverage area of 80% with potential for future improvements.
Application of Machine Learning in Urban Base Station Placement for 5G Communications and Beyond