Will SDN Be Part of 5G?

Zaidi, Zainab; Friderikos, Vasilis; Yousaf, Zarrar; Fletcher, Simon; Dohler, Mischa; Aghvami, Hamid

doi:10.1109/comst.2018.2836315

Cited by 95 publications

(39 citation statements)

References 158 publications

(439 reference statements)

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“…Some initiatives for embedded devices are WiSCoP [35], Atomix [36] and [37]. Recently, there is also a growing interest in academia and industry to apply Software Defined Networking (SDN) and Network Function Virtualization (NFV) to wireless networks [38]. Initiatives such as SoftAir [39], Cloud RAN [40], OpenRadio [41] and several others are still at the conceptual or prototype level.…”

Section: Flexible Spectrum Managementmentioning

confidence: 99%

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

et al. 2018

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This paper presents end-to-end learning from spectrum data -an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to (i) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments, and (ii) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this article is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated (i) modulation recognition and (ii) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation and the frequency domain representation. From our analysis we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.

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Section: Flexible Spectrum Managementmentioning

confidence: 99%

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

et al. 2018

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“…This solution can be an enabler for a network with flexible functional splits, where the network functions are adapted according to a certain set of requirements and enabled when required by NFV. In [115] fronthaul is identified as a major element of SDN-based mobile network architectures.…”

Section: Flexible Functional Splitsmentioning

confidence: 99%

“…[76] presents a simulation considering split option 7-1, where a 10 MHz carrier has the fronthaul latency limit of 0.2 ms and a 5 MHz carrier has the fronthaul latency limit of 0.25 ms. The fronthaul latency is also considered in [115]. The distance between a DU and the CU-pool is determined by the latency.…”

Section: A Fronthaul Latencymentioning

confidence: 99%

A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks

Larsen

Checko²,

Christiansen

2019

IEEE Commun. Surv. Tutorials

306

211

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Pacing the way towards 5G has lead researchers and industry in the direction of centralized processing known from Cloud-Radio Access Networks (C-RAN). In C-RAN research, a variety of different functional splits is presented by different names and focusing on different directions. The functional split determines how many Base Station (BS) functions to leave locally, close to the user, with the benefit of relaxing fronthaul network bitrate and delay requirements, and how many functions to centralize with the possibility of achieving greater processing benefits. This work presents for the first time a comprehensive overview systematizing the different work directions for both research and industry, while providing a detailed description of each functional split option and an assessment of the advantages and disadvantages. This work gives an overview of where the most effort has been directed in terms of functional splits, and where there is room for further studies. The standardization currently taking place is also considered and mapped into the research directions. It is investigated how the fronthaul network will be affected by the choice of functional split, both in terms of bitrates and latency, and as the different functional splits provide different advantages and disadvantages, the option of flexible functional splits is also looked into.

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“…For what concerns the terrestrial networks, reuse of 5G solutions is considered pivotal. Following the literature, enabling technologies being adopted in 5G networks such as software‐defined networking (SDN) and network function virtualization (NFV) represent key technological enablers for improved and more flexible network hybridization . The “smart” 5G architecture allows for network slicing serving various use cases.…”

Section: Satellite Potential and Challenges For 5g Service Provisionmentioning

confidence: 99%

“…For what concerns the terrestrial networks, reuse of 5G 4 enabling technologies being adopted in 5G networks such as software-defined networking (SDN) and network function virtualization (NFV) represent key technological enablers for improved and more flexible network hybridization. 31 The "smart" 5G architecture allows for network slicing serving various use cases. Satellite is adopting the technology paradigms and standards of the 5G community including in the areas of service delivery, IP-based traffic, network-slicing, orchestration, mobile edge computing (MEC), 32 security, interoperability, and resource virtualization in order to transparently support end-to-end service delivery to the verticals.…”

Section: Ground Segment Aspectsmentioning

confidence: 99%

Future technologies for very high throughput satellite systems

Gaudenzi

Angeletti

Petrolati

et al. 2019

Satell Commun Network

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Summary In this paper, we investigate the possible satellite's role and technical solutions required for providing broadband services complementing the terrestrial fifth generation wireless standards. First, we review the satellite networks use cases, services, and system requirements. Then we investigate the satellite potential purpose for broadband service provision and the associated challenges with focus on the system and space/ground technological aspects. Different architectural satellite network solutions are illustrated jointly with the key system design trade‐offs. Particular emphasis is dedicated to the payload architecture. Finally, we illustrate the space and ground segment technologies enabling the successful satellite contribution to broadband services provision.

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Will SDN Be Part of 5G?

Cited by 95 publications

References 158 publications

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications

A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks

Future technologies for very high throughput satellite systems

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