An in-depth study of LTE

HuangJunxian,; Qian-Feng,; Guo, Yihua; ZhouYuanyuan,; XuQiang,; Morley, MaoZ.; SenSubhabrata,; SpatscheckOliver,

doi:10.1145/2534169.2486006

Cited by 118 publications

(6 citation statements)

References 29 publications

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“…The improvements in the radio area network (RAN) have relatively increased the impact of the core network on the end-to-end quality. Huang et al showed that the RAN latency no longer dominates the overall endto-end latency even in LTE networks [20]. The performance requirements for 6G are 100 Gbps -1 Tbps peak data rate and a radio latency of 0.1 ms [1], [17], which are comparable to fixed networks.…”

Section: B 6g Network Architecturementioning

confidence: 99%

Challenges of CPS/IoT Network Architecture in 6G Era

Kato,

Fukumoto,

Sasaki

et al. 2024

IEEE Access

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Cyber Physical System (CPS) and Internet of Things (IoT) have attracted attention as a promising technology for the future society. Although the 6th generation mobile system is expected to work as the CPS/IoT platform, the specific technical requirements are not studied thoroughly. In this paper, we take application driven approach focusing on three applications, eHealth, smart manufacturing, and smart city, to identify mandatory technical requirements for the 6G mobile core network architecture to realize CPS/IoT systems. Specifically, this paper gives the following three contributions. First, we investigate several use cases in the applications listed above and list up the requirements for the core network, i.e., the massive connectivity, the flexible SLA, the ambient IoT device accommodation, the wireless sensing function, and the AI-aware data sharing. Second, we identify the issues to satisfy those requirements in the current 5G technologies. Lastly, we pick up the 6G architectures proposed currently and discuss their pros and cons based on the requirements and issues pointed out.

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Section: B 6g Network Architecturementioning

confidence: 99%

Challenges of CPS/IoT Network Architecture in 6G Era

Kato,

Fukumoto,

Sasaki

et al. 2024

IEEE Access

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“…Communication is a crucial bottleneck in federated networks, compounded by concerns about privacy regarding the transmission of raw data, necessitating the need for local data retention on each device. In fact, federated networks can comprise a multitude of devices, such as millions of smartphones, where network communication can be orders of magnitude slower than local computation [37,38]. Therefore, to accommodate data generated by devices within a federated network, it is imperative to develop efficient communication methods that involve sending small messages or model updates iteratively as part of the training process rather than transmitting the entire dataset over the network.…”

Section: Communication Requirementsmentioning

confidence: 99%

A Review of Federated Meta-Learning and Its Application in Cyberspace Security

Liu,

Li,

Liu

et al. 2023

Electronics

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In recent years, significant progress has been made in the application of federated learning (FL) in various aspects of cyberspace security, such as intrusion detection, privacy protection, and anomaly detection. However, the robustness of federated learning in the face of malicious attacks (such us adversarial attacks, backdoor attacks, and poisoning attacks) is weak, and the unfair allocation of resources leads to slow convergence and inefficient communication efficiency regarding FL models. Additionally, the scarcity of malicious samples during FL model training and the heterogeneity of data result in a lack of personalization in FL models. These challenges pose significant obstacles to the application of federated learning in the field of cyberspace security. To address these issues, the introduction of meta-learning into federated learning has been proposed, resulting in the development of federated meta-learning models. These models aim to train personalized models for each client, reducing performance discrepancies across different clients and enhancing model fairness. In order to advance research on federated meta-learning and its applications in the field of cyberspace security, this paper first introduces the algorithms of federated meta-learning. Based on different usage principles, these algorithms are categorized into client-level personalization algorithms, network algorithms, prediction algorithms, and recommendation algorithms, and are thoroughly presented and analyzed. Subsequently, the paper divides current cyberspace security issues in the network domain into three branches: information content security, network security, and information system security. For each branch, the application research methods and achievements of federated meta-learning are elucidated and compared, highlighting the advantages and disadvantages of federated meta-learning in addressing different cyberspace security issues. Finally, the paper concludes with an outlook on the deep application of federated meta-learning in the field of cyberspace security.

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“…A lightweight passive tool has been introduced for the identification of present LTE networks flaw ("used bandwidth less than 50 percent of the available bandwidth"). They suggest a remote control mechanism by using a more LTE friendly transport protocol and applications [21]. In [22], LTE-Advanced features have been highlighted to improve the quality of service (QoS) specifically focusing on network capacity delivery in terms of offloading and congestion control.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation

Zulqarnain

Lee

2021

Applied Sciences

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These days, autonomous vehicles (AVs) technology has been improved dramatically. However, even though the AVs require no human intervention in most situations, AVs may fail in certain situations. In such cases, it is desirable that humans can operate the vehicle manually to recover from a failure situation through remote driving. Furthermore, we believe that remote driving can enhance the current transportation system in various ways. In this paper, we consider a revolutionary transportation platform, where all the vehicles in an area are controlled by some remote controllers or drivers so that transportation can be performed in a more efficient way. For example, road capacity can be effectively utilized and fuel efficiency can be increased by centralized remote control. However, one of the biggest challenges in such remote driving is the communication latency between the remote driver and the vehicle. Thus, selecting appropriate locations of the remote drivers is very important to avoid any type of safety problem that might happen due to large communication latency. Furthermore, the selection should reflect the traffic situation created by multiple vehicles in an area. To tackle these challenges, in this paper, we propose several algorithms that select remote drivers’ locations for a given transportation schedules of multiple vehicles. We consider two objectives in this system and evaluate the performance of the proposed algorithms through simulations. The results show that the proposed algorithms perform better than some baseline algorithms.

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An in-depth study of LTE

Cited by 118 publications

References 29 publications

Challenges of CPS/IoT Network Architecture in 6G Era

Challenges of CPS/IoT Network Architecture in 6G Era

A Review of Federated Meta-Learning and Its Application in Cyberspace Security

Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation

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