1,882 research outputs found
Self-Evolving Integrated Vertical Heterogeneous Networks
6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table
Energy-Efficient Softwarized Networks: A Survey
With the dynamic demands and stringent requirements of various applications,
networks need to be high-performance, scalable, and adaptive to changes.
Researchers and industries view network softwarization as the best enabler for
the evolution of networking to tackle current and prospective challenges.
Network softwarization must provide programmability and flexibility to network
infrastructures and allow agile management, along with higher control for
operators. While satisfying the demands and requirements of network services,
energy cannot be overlooked, considering the effects on the sustainability of
the environment and business. This paper discusses energy efficiency in modern
and future networks with three network softwarization technologies: SDN, NFV,
and NS, introduced in an energy-oriented context. With that framework in mind,
we review the literature based on network scenarios, control/MANO layers, and
energy-efficiency strategies. Following that, we compare the references
regarding approach, evaluation method, criterion, and metric attributes to
demonstrate the state-of-the-art. Last, we analyze the classified literature,
summarize lessons learned, and present ten essential concerns to open
discussions about future research opportunities on energy-efficient softwarized
networks.Comment: Accepted draft for publication in TNSM with minor updates and editin
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Introduction to the Special Section on Emerging Technologies for Connected Vehicles and ITS Networks
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