Performance Prediction of Power Beacon-Aided Wireless Sensor-Powered Non-Orthogonal Multiple-Access Internet-of-Things Networks under Imperfect Channel State Information
Source abstract
Abstract
In this paper, we investigate a novel power beacon (PB)-aided wireless sensor-powered non-orthogonal multiple-access (NOMA) Internet-of-Things (IoT) network under imperfect channel state information (CSI). Furthermore, the exact expression outage probability (OP) of two IoT users is derived to analyze the performance of the considered network. To give further insight, the expression asymptotic OP and diversity order are also expressed when the transmit power at the PB goes to infinity. Furthermore, a deep neural network (DNN) framework is proposed to concurrently forecast IoT users’ OP in relation to real-time setups for IoT users. Additionally, when compared to the traditional analysis, our created DNN shows the shortest run-time prediction, and the outcomes predicted by the DNN model almost match those of the simulation. In addition, numerical results validate our analysis, simulation, and prediction through a Monte Carlo Simulation. Furthermore, the results show the impact of the main parameter on our proposed system. Finally, these findings show that NOMA performs better than the conventional orthogonal multiple-access (OMA) techniques.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Harvesting in Wireless Networks · Advanced Wireless Communication Technologies · Advanced MIMO Systems Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam
Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 70800 Ostrava, Czech Republic
Faculty of Fundamental 2, Posts and Telecommunications Institute of Technology, Ho Chi Minh City 70000, Vietnam
Faculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City 754000, Vietnam
Faculty of Electrical Engineering and Informatics, University of Pardubice, 53210 Pardubice, Czech Republic
Department of Artificial Intelligence, Korea National University of Transportation (KNUT), Uiwang-si 16106, Gyeonggi-do, Republic of Korea
Funding
National Research Foundation of Korea (NRF) · Korea Institute of Energy Technology Evaluation and Planning (KETEP) · Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea
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