Salam, rekan Nawala! Semoga kalian selalu dalam keadaan sehat.
Ini adalah Nawala IAES dari Institute of Advanced Engineering and Science. Hari ini kami akan berbagi kabar tentang teknologi unmanned aerial vehicle (UAV). UAV adalah pesawat yang beroperasi tanpa pilot manusia di dalamnya, atau biasa dikenal dengan pesawat tanpa awak. Kendaraan ini sering juga dikenal sebagai drone dan dapat dikendalikan dari jarak jauh oleh operator manusia. Selain itu juga dapat beroperasi secara otonom berdasarkan rencana penerbangan yang telah diprogram sebelumnya atau menggunakan sistem kecerdasan buatan. Perkembangan UAV memandu para peneliti untuk berkonsentrasi pada penyelesaian masalah seperti menangani informasi yang kompleks dan berskala besar serta komunikasi yang tidak terputus. Thangaraj dan Sangam (2023) telah melakukan penelitian untuk menangani masalah-masalah tersebut menggunakan artificial neural network (ANN) dan artificial potential field (APF). Hasil yang lebih detail dari penelitian tersebut dapat dilihat pada:
Unmanned aerial vehicles (UAVs) are utilized extensively in various fields of daily activities in the day to day life and industrial applications. The raises of utilization of UAVs guide the researchers to concentrate on various problems like handling rich and large-scale information and uninterrupted communication. Further, to achieve the above the obstacle free zone is mandatory and the present autonomous drones may fail to handle such situations. To address the mentioned issues, an effective path planning algorithm is needed, to find the optimal path and obstacle free mobility. Hence, UAV path planning needs intelligent and autonomous navigation system by providing high level of optimization in order to attain optimal path with the obstacles avoidance. In this paper, AI employed framework for UAV path planning is proposed by utilizing the salient features of both artificial neural network (ANN) and artificial potential field (APF). ANN is implemented for obtaining optimal path and APF is utilized for evading the obstacles throughout the path. Further, the implementation results show the better performance than the existing works in terms of the collision free optimal path for UAVs.
Intelligent UAV path planning framework using artificial neural network and artificial potential field
Meena Thangaraj, Ravi Sankar Sangam
Perkembangan teknologi UAV tidak terbatas pada pengembangan perangkat itu sendiri. Ai dkk. (2023) mengembangkan perangkat pendukung UAV, yang disebut dengan reconfigurable intelligent surfaces (RISs) assisted. Dengan dikembangkannya RIS-assisted, hal itu dapat meminimalisir tingkat eror (error-rate) pada perangkat UAV. Selain itu, RIS-assisted juga dapat meningkatkan cakupan dan keandalan perangkat UAV. Penelitian tersebut dapat dilihat secara lengkap pada artikel berikut:
In this study, to reduce the average symbol error rate, and improve coverage and reliability of unmanned aerial vehicles (UAVs) to ground communication systems. In this case, we propose a reconfigurable intelligent surfaces (RISs) assisted for UAV to ground communication scheme, where radio frequency (RF) signal generator sends an unmodulated carrier signal from UAVs to the RIS. At reconfigurable intelligent surface, the RIS modulates each signal, and RIS is as a signal generator. We carry out a performance analysis of UAV-to-ground communication systems with RIS-assisted and without RIS for subcarrier quadrature amplitude modulation (SC-QAM) technique. The analytical expressions of average symbol error rare (ASER) and average channel capacity (ACC) is derived. From the results, it is show that with RIS assisted can effectively improve the reliability and coverage of the UAVs to ground communication systems.
On the performance of reconfigurable intelligent surface-assisted UAV-to-ground communication systems
Duong Huu Ai, Van Loi Nguyen, Hoang Huu Duc, Khanh Ty Luong
Do dan Le (2022) melakukan penelitian terkait integrasi antara non-orthogonal multiple access (NOMA) dan UAV. Mereka membahas analisis kinerja dari skenario di mana UAV berkomunikasi dalam jaringan cognitive radio berbasis NOMA (CR-NOMA) dengan dua pengguna tujuan, yakni sebuah perangkat seluler dan sebuah tujuan utama yang mengikuti distribusi Nakagami-m fading. Penjelasan lebih lengkap mengenai hasil yang didapatkan dapat dilihat pada artikel berikut:
We highlight the potential of non-orthogonal multiple access (NOMA) integration with unmanned aerial vehicle (UAV) for future communications networks in beyond 5 generation (B5G) networks to enhance cellular communication, support massive connections and increase data rates. We consider a scenario, where a UAV communicates in a downlink underlay cognitive radio based NOMA network (CR-NOMA) with two destination users, a cellular device, and a primary destination following Nakagami-m fading distribution. We study the impact of perfect and imperfect successive interference cancellation (SIC) on outage probability (OP). To help analyze this impact, we derive exact outage results for different network users under perfect and imperfect SIC conditions. Consequently, we make use of Monte Carlo simulations to confirm the analytical results.
UAV-assisted underlay CR-NOMA network: performance analysis
Dinh-Thuan Do, Chi-Bao Le
Beberapa artikel di atas merupakan bagian kecil dari penelitian mengenai teknologi unmanned aerial vehicle (UAV). Untuk mendapatkan informasi lebih lanjut, pembaca dapat mengunjungi laman dan membaca artikel secara GRATIS melalui tautan-tautan berikut: https://ijeecs.iaescore.com/, http://telkomnika.uad.ac.id/, dan https://beei.org/.
Redaksi: I. Busthomi