USING FEDERATED MACHINE LEARNING FOR ACCESS POINT SELECTION IN THE HETEROGENEOUS NETWORK

2021 ◽  
Author(s):  
Р.Я. ПИРМАГОМЕДОВ
Keyword(s):  
Machine Learning ◽  
Heterogeneous Network ◽  
Access Network ◽  
Access Point ◽  
Learning System ◽  
Wireless Access ◽  
Network Selection ◽  
Point Selection ◽  
Wireless Access Network ◽  
Series Of Experiments

The problem of selecting a wireless access network in a highly heterogeneous environment has been analyzed and solved. A network selection model based on the analysis of a wireless network environment using a federated reinforcement machine learning system is proposed. A model has been developed to estimate the theoretical average capacity available to the user in a highly heterogenic access network. The effectiveness of the proposed method was evaluated using a series of experiments. The article is concluded with a discussion regarding the applicability of the proposed method for IMT-2020 and IMT-2030 networks.

Wireless Access Network Selection Game with Negative Network Externality

2013 ◽  
Vol 12 (10) ◽  
pp. 5048-5060 ◽  
Author(s):  
Yu-Han Yang ◽  
Yan Chen ◽  
Chunxiao Jiang ◽  
Chih-Yu Wang ◽  
K. J. Ray Liu
Keyword(s):  
Access Network ◽  
Network Externality ◽  
Wireless Access ◽  
Network Selection ◽  
Wireless Access Network ◽  
Access Network Selection

Performance Evaluation of Fiber Wireless (FiWi) Access Network using Position Optimization of ONUs

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Chouhan ◽  
Uma Rathore Bhatt ◽  
Raksha Upadhyay
Keyword(s):  
Power Consumption ◽  
Optimization Algorithm ◽  
Optical Network ◽  
Access Network ◽  
Whale Optimization Algorithm ◽  
Wireless Access ◽  
Delivery Ratio ◽  
Packet Delivery ◽  
Whale Optimization ◽  
Wireless Access Network

: Fiber Wireless Access Network is the blend of passive optical network and wireless access network. This network provides higher capacity, better flexibility, more stability and improved reliability to the users at lower cost. Network component (such as Optical Network Unit (ONU)) placement is one of the major research issues which affects the network design, performance and cost. Considering all these concerns, we implement customized Whale Optimization Algorithm (WOA) for ONU placement. Initially whale optimization algorithm is applied to get optimized position of ONUs, which is followed by reduction of number of ONUs in the network. Reduction of ONUs is done such that with fewer number of ONUs all routers present in the network can communicate. In order to ensure the performance of the network we compute the network parameters such as Packet Delivery Ratio (PDR), Total Time for Delivering the Packets in the Network (TTDPN) and percentage reduction in power consumption for the proposed algorithm. The performance of the proposed work is compared with existing algorithms (deterministic and centrally placed ONUs with predefined hops) and has been analyzed through extensive simulation. The result shows that the proposed algorithm is superior to the other algorithms in terms of minimum required ONUs and reduced power consumption in the network with almost same packet delivery ratio and total time for delivering the packets in the network. Therefore, present work is suitable for developing cost-effective FiWi network with maintained network performance.

scholarly journals Joint optimization of network selection and task offloading for vehicular edge computing

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Lujie Tang ◽  
Bing Tang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Large Scale ◽  
Vehicular Networks ◽  
Access Point ◽  
Edge Computing ◽  
Network Selection ◽  
Network Access ◽  
Task Execution ◽  
Point Selection ◽  
Vehicle Movement ◽  
Task Offloading

AbstractTaking the mobile edge computing paradigm as an effective supplement to the vehicular networks can enable vehicles to obtain network resources and computing capability nearby, and meet the current large-scale increase in vehicular service requirements. However, the congestion of wireless networks and insufficient computing resources of edge servers caused by the strong mobility of vehicles and the offloading of a large number of tasks make it difficult to provide users with good quality of service. In existing work, the influence of network access point selection on task execution latency was often not considered. In this paper, a pre-allocation algorithm for vehicle tasks is proposed to solve the problem of service interruption caused by vehicle movement and the limited edge coverage. Then, a system model is utilized to comprehensively consider the vehicle movement characteristics, access point resource utilization, and edge server workloads, so as to characterize the overall latency of vehicle task offloading execution. Furthermore, an adaptive task offloading strategy for automatic and efficient network selection, task offloading decisions in vehicular edge computing is implemented. Experimental results show that the proposed method significantly improves the overall task execution performance and reduces the time overhead of task offloading.

Sign in / Sign up

Export Citation Format

Share Document