Feature extraction of trajectories for mobility modeling in 5G NB-IoT networks

Zhang, Runzhou; Ning, Lei; Li, Mengkun; Wang, Chengcai; Li, Wei; Wang, Yongjian

doi:10.1007/s11276-021-02742-8

Cited by 2 publications

(2 citation statements)

References 45 publications

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“…However, uRLLC was not considered except in the discussion of network slicing, but joint eMBB-uRLLC scheduling was not investigated. Similarly, uRLLC slices were mentioned in [17,18], where the focus is on 5 G vehicular scenarios (V2X), whereas [19] investigated 5 G IoT scenarios.…”

Section: Related Workmentioning

confidence: 99%

Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks

Al-Ali

Yaacoub

2023

Wireless Netw

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Abstract5G technology is intended to support three promising services with heterogeneous requirements: Ultra-Reliable and Low Latency Communication (uRLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communication (mMTC). 6G is required to support even more challenging scenarios, including the presence of a large number of uRLLC devices, under the massive uRLLC (mURLLC) use case scenario. The presence of these services on the same network creates a challenging task of resource allocation to meet their diverse requirements. Given the critical nature of uRLLC applications, uRLLC traffic will always have the highest priority which causes a negative impact on the performance of other services. In this paper, the problem of uRLLC/eMBB resource allocation is investigated. An optimal resource allocation scheme is proposed with two scenarios including a guaranteed fairness level and minimum data rate among eMBB users. In addition, a knapsack-inspired punctured resource allocation algorithm is proposed where the users’ channel qualities of both services are considered at each time slot leading to the most suitable Resource Block (RB) selection for puncturing in a way that minimizes the negative impact on eMBB performance. The proposed solution was compared with three puncturing baseline reference algorithms and the performance was evaluated in terms of eMBB Sum throughput and Fairness level. The simulation results show that the proposed algorithm outperforms the above-mentioned reference algorithms in all evaluation metrics and is proved to be comparable to the optimal solution given its low complexity.

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Section: Related Workmentioning

confidence: 99%

Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks

Al-Ali

Yaacoub

2023

Wireless Netw

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“…With the advancement of wireless technology, Wi-Fi-enabled intelligent devices (e.g., cameras, air conditioners, smart audio, etc.) have been deployed everywhere [1], with many Wi-Fi devices in homes and offices. No matter what environment we are in, the radio frequency (RF) signals emitted by these devices surround us all the time.…”

Section: Introductionmentioning

confidence: 99%

TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device

Zhang,

Hao,

Dang

et al. 2023

Applied Sciences

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With the popularization of Wi-Fi router devices, the application of device-free sensing has garnered significant attention due to its potential to make our lives more convenient. Wi-Fi signal-based through-the-wall human detection offers practical applications, such as emergency rescue and elderly monitoring. However, the accuracy of through-the-wall human detection is hindered by signal attenuation caused by wall materials and multiple propagation paths of interference. Therefore, through-the-wall human detection presents a substantial challenge. In this paper, we proposed a highly robust through-the-wall human detection method based on a commercial Wi-Fi device (TwSense). To mitigate interference from wall materials and other environmental factors, we employed the robust principal component analysis (OR-PCA) method to extract the target signal of Channel State Information (CSI). Subsequently, we segmented the action-induced Doppler shift feature image using the K-means clustering method. The features of the images were extracted using the Histogram of Oriented Gradients (HOG) algorithm. Finally, these features were fed into an SVM classifier (G-SVM) optimized by a grid search algorithm for action classification and recognition, thereby enhancing human detection accuracy. We evaluated the robustness of the entire system. The experimental results demonstrated that TwSense achieved the highest accuracy of 96%.

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Feature extraction of trajectories for mobility modeling in 5G NB-IoT networks

Cited by 2 publications

References 45 publications

Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks

Resource allocation scheme for eMBB and uRLLC coexistence in 6G networks

TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device

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