Abstract:LTE (Long-Term Evolution) is one of the appropriate technologies to sever the requirement of the IoT (Internet of Things) together with mobile UE (User Equipment), because of priority services, high data rate, low latency, etc. LTE has received OFDM for the transmission because it meets the necessity of LTE-like range flexibility and empowers the cost gainful responses for the wide carriers. LTE offers big-data values, allows spectrum reframing, reduces cost, etc. We proposed a 4G/5G adopted OFDM based on the … Show more
“…In [13], an analytical model compared multipath and single-path CBRP routing protocols in Mobile Ad-hoc Networks (MANETs), demonstrating that multipath routing reduces network congestion and improves end-to-end delay and queue length [13]. Authors in [14] attempted to improve the performance of 4G/5G wireless systems by implementing wavelets for data transmission. Authors in [15] explored the efficient allocation of slices in 5G core networks.…”
The rise of 5G technology has brought with it a surge in diverse services with demanding and varying requirements. Network fragmentation has emerged as a critical technique to address this challenge, enabling the creation of virtual network segments on a shared infrastructure, allowing for efficient resource utilization and improved performance. This paper investigates the potential of network fragmentation, combined with optimized resource allocation, to enhance the performance of 5G core networks. A novel framework that integrates these two techniques is proposed. The former takes into account factors, such as network traffic patterns, service requirements, and resource availability. The framework aims to optimize network performance metrics, namely throughput, latency, and resource utilization. The experimental results demonstrate the effectiveness of the proposed framework, showcasing a significant improvement in overall network performance, paving thus the way for efficient and robust 5G service delivery.
“…In [13], an analytical model compared multipath and single-path CBRP routing protocols in Mobile Ad-hoc Networks (MANETs), demonstrating that multipath routing reduces network congestion and improves end-to-end delay and queue length [13]. Authors in [14] attempted to improve the performance of 4G/5G wireless systems by implementing wavelets for data transmission. Authors in [15] explored the efficient allocation of slices in 5G core networks.…”
The rise of 5G technology has brought with it a surge in diverse services with demanding and varying requirements. Network fragmentation has emerged as a critical technique to address this challenge, enabling the creation of virtual network segments on a shared infrastructure, allowing for efficient resource utilization and improved performance. This paper investigates the potential of network fragmentation, combined with optimized resource allocation, to enhance the performance of 5G core networks. A novel framework that integrates these two techniques is proposed. The former takes into account factors, such as network traffic patterns, service requirements, and resource availability. The framework aims to optimize network performance metrics, namely throughput, latency, and resource utilization. The experimental results demonstrate the effectiveness of the proposed framework, showcasing a significant improvement in overall network performance, paving thus the way for efficient and robust 5G service delivery.
“…6 ., the sample outputs obtained through the Implementation of the proposed method is shown. From the dataset, the sample outputs for facial expressions give sadness, sadness, and fear depending on its emotion score for each emotion [ 19 ]. Fig.…”
“…These neighbors are chosen from a selection of educational websites with the right established categories. KNN regionally approximates the feature and waits to compute it because it is a lazy studying collection of rules [42].…”
Section: (B) Elucidation Of ML Algorithmsmentioning
Electrified transportation systems are emerging quickly worldwide, helping to diminish carbon gas emissions and paving the way for the reduction of global warming possessions. Battery remaining useful life (RUL) prediction is gaining attention in real world applications to tone down maintenance expenses and improve system reliability and efficiency. RUL forms the prominent component of fault analysis forecast and health management when the equipment operation life cycle is considered. The uprightness of RUL prediction is vital in providing the effectiveness of electric batteries and reducing the chance of battery illness. In assessing battery performance, the existing prediction approaches are unsatisfactory even though the battery operational parameters are well tabulated. In addition, battery management has an important contribution to several sustainable development goals, such as Clean and Affordable Energy (SDG 7), and Climate Action (SDG 13). The current work attempts to increase the prediction accuracy and robustness with selected machine learning algorithms. A Real battery life cycle data set from the Hawaii National Energy Institute (HNEI) is used to evaluate accuracy estimation using selected machine learning algorithms and is validated in Google Co-laboratory using Python. Evaluated error metrics such as Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), R-Squared, and execution time are computed for different L methods and relevant inferences are presented which highlight the potential of battery RUL prediction close to the most accurate values.
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