New Insight into Network Virtualization and Management

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 1096

Special Issue Editor


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Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: computer networking; network virtualization; distributed systems; machine learning

Special Issue Information

Dear Colleagues,

Network function virtualization (NFV and software-defined networking (SDN) are playing a fundamental role in the softwarization of the network infrastructure. Both technologies have revolutionized the way network functions and services are offered, improving the performance and privacy while providing better agility and significant cost reduction. The present and future application instances are often deployed as virtualized assets within software-defined infrastructures and managed (and made available) under the “as a Service” paradigm. 

New methodologies for managing these virtualized and softwarized infrastructures are therefore needed. For example, machine learning (ML) and artificial intelligence (AI) can harness the immense amount of operational data from clouds to services as well as social and communication networks. In the era of big data and connected devices of all varieties, ML/AI has found ways to improve the operations and management of information technology networks, systems, and services. However, further research is needed to understand and improve the potential and suitability of the new mechanisms to face challenges related to the new softwarized infrastructures for faster and improved deployment, as well as reactive mechanisms, for services in 5G and beyond environments. Furthermore, the increasing need for the achievement of a computing continuum from IoT to edge to cloud poses new challenges in network management that demand new management trends.

Topics of interest for this Special Issue include, but are not limited to:

  • Cross-domain network infrastructures, federation, interconnection;
  • Effective integration in software-based control, multi-domain management, and orchestration;
  • Effective services integration in network programmability;
  • Energy efficiency and green operation in virtualized and softwarized networks;
  • ML/AI, neural networks, and deep learning for management;
  • Data mining, statistical modeling, and machine learning for management.

Dr. Sacco Alessio
Guest Editor

Manuscript Submission Information

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Keywords

  • network virtualization
  • network management
  • SDN
  • machine learning
  • edge continuum

Published Papers (1 paper)

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Research

16 pages, 4045 KiB  
Article
SWIPT Cooperative Protocol for Improving the Communication Quality of Cell-Edge Users in NOMA Network and Its Performance Analysis
by Suoping Li, Tongtong Jia, Hailing Yang, Ruiman Gao and Qian Yang
Electronics 2023, 12(17), 3583; https://doi.org/10.3390/electronics12173583 - 24 Aug 2023
Viewed by 709
Abstract
To solve the problems of complex energy supply and poor communication effect of cell-edge users, combining the advantages of simultaneous wireless information and power transfer (SWIPT) and collaborative non-orthogonal multiple access (CNOMA), two novel protocols are proposed. By employing the PS and TS [...] Read more.
To solve the problems of complex energy supply and poor communication effect of cell-edge users, combining the advantages of simultaneous wireless information and power transfer (SWIPT) and collaborative non-orthogonal multiple access (CNOMA), two novel protocols are proposed. By employing the PS and TS strategies in SWIPT, respectively, two protocols are named: SWIPT-CNOMA-PS and SWIPT-CNOMA-TS. Based on the protocols, a new method for selecting relays is first established by considering two factors—energy state and channel condition. To further improve energy efficiency, the relay harvests the energy of the signal sent by the source and stores it. We establish a one-dimensional Markov chain with the energy state as the system state to analyze the variation of the relay energy. Exploiting the conservation equation, the probability of energy sufficiency of the node and outage probabilities of users are derived. Finally, the impact of the energy threshold and the number of relays on the outage probability of users is revealed. The protocol in this paper shows better performance than the OMA protocol when the transmit signal-to-noise ratio (SNR) is less than 14 dB and the protocol in the literature when the transmit SNR is less than 11 dB. Full article
(This article belongs to the Special Issue New Insight into Network Virtualization and Management)
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