Performance Modelling and Optimization in Future Networks

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 1059

Special Issue Editors

School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UK
Interests: mobile edge computing; software-defined networking; network function virtualization; AI/ML-driven resource optimization; performance modeling and analysis
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Guest Editor
School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Plymouth PL4 8AA, UK
Interests: evolutionary computation; visualisation; data science; artificial intelligence; hydroinformatics
Multimedia Communication and Intelligent Control, School of Engineering, Computing and Mathematics, Faculty of Science and Engineering, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
Interests: prediction and control of video quality using AI, ML, cloud computing, fuzzy logic, applying computer vision techniques, and deep learning in pedestrian recognition; disease identification in cotton crops and damage recognition in wind turbines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The proliferation of the skyrocketing number and types of mobile and IoT devices, coupled with large volumes of network traffic data (e.g., audio, picture, and video) which these devices generate, have brought great challenges to the traditional network systems to support emerging applications and services. This include areas as autonmous driving, virtual reality, remote surgury, and so on. To address these challenges issue, a series of novel technologies, such as software-defined networking (SDN), network function virtualisation (NFV), information-centric networking (ICN), artifical intelligence, cloud/edge/device computing and blockchain, have been proposed or exploited to revolutionise the operation of network systems towards being more intelligent, autonmous, reliable and sustanable. These evolutions will bring a series of benefits to future networks, such as reduced captial expenditure and operational expenditure for network management, shortened lifecycle of network innovation required to deploy new services, as well asmore agile and flexible network service provisioning. However, the transformation of network intelligence, softerisation, virtualisation and cloudification is also married with the new challenge of how to efficiently plan, control and orchestrate network resources in large-scale, dynamic and complex operation environments, where multiple potential conflicted Quality-of-Service (QoS) requirements, such as latency, throughput, realibility, avaliability and energy efficiency, should be simutaneously met. In this regard, this Special Issue focuses on the advances of the QoS modelling and resource optimisation in future networks. We aim to provide a platform for researchers from academia and industry to present their novel and unpublished work in the domain of future network resource management and optimisation. The research areas of this Special Issue include, but not limited to:

  • Software-defined metworking
  • Network function virtalisation
  • Information-centric networks
  • Intent-based networking
  • Cloud/Edge/Device computing systems
  • Internet of everything
  • Unmanned aerial vehicle networks
  • Wireless sensor networks
  • Distributed ledger systems
  • 5G/6G network and communication systems
  • Machine learning and artifical intelligence-empowered network optimisation.

Dr. Wang Miao
Dr. David Walker
Dr. Asiya Khan
Guest Editors

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Keywords

  • performance evaluation
  • QoS modelling
  • service level agreement
  • future networks
  • ML/AI
  • resource management and optimisation

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Published Papers (1 paper)

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Research

21 pages, 1011 KiB  
Article
TADocs: Teacher–Assistant Distillation for Improved Policy Transfer in 6G RAN Slicing
by Xian Mu, Yao Xu, Dagang Li and Mingzhu Liu
Mathematics 2024, 12(18), 2934; https://doi.org/10.3390/math12182934 - 20 Sep 2024
Viewed by 598
Abstract
Network slicing is an advanced technology that significantly enhances network flexibility and efficiency. Recently, reinforcement learning (RL) has been applied to solve resource management challenges in 6G networks. However, RL-based network slicing solutions have not been widely adopted. One of the primary reasons [...] Read more.
Network slicing is an advanced technology that significantly enhances network flexibility and efficiency. Recently, reinforcement learning (RL) has been applied to solve resource management challenges in 6G networks. However, RL-based network slicing solutions have not been widely adopted. One of the primary reasons for this is the slow convergence of agents when the Service Level Agreement (SLA) weight parameters in Radio Access Network (RAN) slices change. Therefore, a solution is needed that can achieve rapid convergence while maintaining high accuracy. To address this, we propose a Teacher and Assistant Distillation method based on cosine similarity (TADocs). This method utilizes cosine similarity to precisely match the most suitable teacher and assistant models, enabling rapid policy transfer through policy distillation to adapt to the changing SLA weight parameters. The cosine similarity matching mechanism ensures that the student model learns from the appropriate teacher and assistant models, thereby maintaining high performance. Thanks to this efficient matching mechanism, the number of models that need to be maintained is greatly reduced, resulting in lower computational resource consumption. TADocs improves convergence speed by 81% while achieving an average accuracy of 98%. Full article
(This article belongs to the Special Issue Performance Modelling and Optimization in Future Networks)
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