Sökning: "Network Slicing"
Visar resultat 1 - 5 av 6 uppsatser innehållade orden Network Slicing.
1. Adversarial Machine (Deep) Learning-basedRobustification in 5G Networks
Master-uppsats, Luleå tekniska universitet/Institutionen för system- och rymdteknikSammanfattning : A significant development in wireless communication and artificial intelligence has been made possible by the combination of 5G networks with deep learning methods. This paper explores the complex interactions between these areas, concentrating on the dangers that adversarial attacks represent in the context of 5G network slicing. LÄS MER
2. Network Slicing to Enhance Edge Computing for Automated Warehouse
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : In a previous work, a distributed safety framework supported by edge computing was developed to enable real-time response of robots that collaborate with humans in the Human-Robot Collaboration (HRC) scenario. However, as the number of robots in the automated warehouse increases, the network is easier to induce the congestion. LÄS MER
3. Service Level Objective based Fairness
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : To solve the bottleneck problem of resource utilization and user experience quality in mobile communication networks, 5G introduces network slicing to cope with the huge resource demand of users. To further improve the quality of service for users with different needs, a new fairness definition based on service level objective is introduced. LÄS MER
4. AI-driven admission control : with Deep Reinforcement Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 5G is expected to provide a high-performance and highly efficient network to prominent industry verticals with ubiquitous access to a wide range of services with orders of magnitude of improvement over 4G. Network slicing, which allocates network resources according to users’ specific requirements, is a key feature to fulfil the diversity of requirements in 5G network. LÄS MER
5. Random Reference Models and Network Rewiring in Temporal Network Clustering
Kandidat-uppsats, Uppsala universitet/Institutionen för informationsteknologiSammanfattning : Computing on temporal networks is difficult because of their dynamic nature. One way to solve this is to slice them into multilayer networks, but this results in a loss of information. LÄS MER