Experimental Demonstration of a Visible Light Communications System Based on Binary Frequency-Shift Keying Modulation: A New Step toward Improved Noise Resilience

Sensors, Journal Year: 2023, Volume and Issue: 23(11), P. 5001 - 5001

Published: May 23, 2023

Visible light communications (VLC) are an emerging technology that is increasingly demonstrating its ability to provide wireless in areas where radio frequency (RF) might have some limitations. Therefore, VLC systems offer possible answers various applications outdoor conditions, such as the road traffic safety domain, or even inside large buildings, indoor positioning for blind people. Nevertheless, several challenges must still be addressed order obtain a fully reliable solution. One of most important focused on further improving immunity optical noise. Different from works, on–off keying (OOK) modulation and Manchester coding been preferred choices, this article proposes prototype based binary frequency-shift (BFSK) non-return-to-zero (NRZ) coding, which resilience noise compared standard OOK system. The experimental results showed improvement 25% direct exposure incandescent sources. system using BFSK was able maintain maximum irradiance 3500 µW/cm2 with 2800 modulation, almost 20% indirect active link equivalent 65,000 µW/cm2, opposed 54,000 modulation. Based these results, one can see proper design, impressive

Language: Английский

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Reliable cooperative communication in cognitive vehicular networks for intelligent transportation systems

International Journal of Communication Systems, Journal Year: 2023, Volume and Issue: 37(6)

Published: Dec. 30, 2023

Summary Rapid intelligent transportation systems (ITS) innovations need a reliable MAC protocol to enable massive nonsafety message delivery and high‐priority safety broadcasts. The significant rise in spectrum is regulated by collaboration cognitive vehicular networks (CVNs). For QoS improvement, c ooperative for CVN s (CCVN‐MAC) presented this study. CCVN allows vehicles collaborate share channel status information, allowing proactive switching case of legacy user (LU) appearance. To transmission mode selection, additional control signals are included. Using the suggested cooperative makeup technique, helper nodes resend failed transmission. A Markov chain represents protocol, NS‐2 used evaluate it several performance characteristics. Compared with conventional techniques, proposed depicts improved performance, including depreciation 70% average latency an increment 42.4% throughput.

Language: Английский

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Development of a Framework for Predicting Students' Academic Performance in STEM Education using Machine Learning Methods

International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Jan. 1, 2024

In the continuously evolving educational landscape, prediction of students' academic performance in STEM (Science, Technology, Engineering, Mathematics) disciplines stands as a paramount component for stakeholders aiming at enhancing learning methodologies and outcomes. This research paper delves into sophisticated analysis, employing Machine Learning (ML) algorithms to predict achievements, focusing explicitly on multifaceted realm education. By harnessing robust dataset drawn from diverse backgrounds, incorporating myriad factors such historical data, socioeconomic demographics, individual interactions, study innovates by transcending traditional parameters. The meticulously evaluates several machine models, juxtaposing their efficacies through rigorous methodologies, including Random Forest, Support Vector Machines, Neural Networks, subsequently advocating an ensemble approach bolster accuracy. Critical insights reveal that customized pathways, preemptive identification at-risk candidates, nuanced understanding contributing influencers are significantly enhanced ML framework, offering transformative lens strategies. Furthermore, confronts ethical quandaries challenges data privacy emerging wake advanced analytics education, proposing holistic guideline stakeholders. exploration not only underscores potential revolutionizing predictive strategies education but also advocates continuous model optimization, embracing symbiotic integration between pedagogical technological advancements, thereby redefining trajectories paradigms.

Language: Английский

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Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

Deleted Journal, Journal Year: 2024, Volume and Issue: 20(3s), P. 971 - 979

Published: April 4, 2024

The following research focuses on the use of machine learning-based beamforming algorithms to improve Massive Multiple Input Output (MIMO) systems in 5G networks. Four unique namely, Deep Learning Beamforming Algorithm (DLBA), Reinforcement Learning-Based Doa Estimation (RLBEA), Clustering based beam forming algorithm(CBA) and GeneticAlgorithm Based Beam Forming Algoeithm were developed after which each them was undertook evaluation. Widespread trials, a simulated environment, have revealed that DLBA RLBA considerably outperform other technologies by means system throughput SINR as well Both achieved high throughput, increased levels low BER. CBA GABA, using clustering genetic their approaches, displayed moderate values all assessed composite measures. This offers important insights adaptability learning potential machine-learning highlighting ability efficiency wireless communication networks during revolution

Language: Английский

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Integrated control strategy for 5G base station frequency regulation considering spatio-temporal variations in communication load

Journal of Energy Storage, Journal Year: 2024, Volume and Issue: 95, P. 112535 - 112535

Published: June 19, 2024

Language: Английский

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