Energy-Efficient SON-Based User-Centric Backhaul Scheme

Jaber, Mona; Imran, Muhammad Ali; Tafazolli, Rahim; Tukmanov, Anvar

doi:10.1109/wcncw.2017.7919074

Cited by 4 publications

(16 citation statements)

References 8 publications

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“…Some of its applications include: radio parameters configuration (self-configuration) [186]- [188], caching [189], backhaul optimization [190]- [193], coverage and capacity optimization [186]- [188], [194], [195], HO parameters optimization [196]- [198], load balancing [175], [199]- [201], resource optimization [193], [202]- [207] and cell outage management (self-healing) [49], [71], [186]- [188], [208], [209].…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

“…In the future, however, as systems will require to support a wider range of applications and different types of data, this approach might not be suitable and a more end-to-end approach, considering the whole link between the user and the core network might be better. With that in mind, some researchers developed solutions in order to solve the backhaul problem in future networks in terms of QoS and QoE provisioning [190]- [193], congestion management [6], [252] and also topology management [164].…”

Section: A Backhaulmentioning

confidence: 99%

“…Other backhaul optimization solutions are the works proposed by Jaber et al in [190]- [193]. In these works the authors used QL to intelligently associate users with different requirements, in terms of capacity, latency and resilience, to small cells depending on the backhaul connection that they offered.…”

Section: A Backhaulmentioning

confidence: 99%

“…Recent work by Jaber et al, in [193], tried to intelligently associate users with different BSs depending on their backhaul connections. In the proposed scenario, each BS had multiple backhaul connections and an energy optimization, in terms of which backhaul links to turn on and off, was performed.…”

Section: G Resource Optimizationmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

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417

200

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Abstract-In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future.

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Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Section: A Backhaulmentioning

confidence: 99%

Section: A Backhaulmentioning

confidence: 99%

Section: G Resource Optimizationmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

Klaine

Imran

Onireti

et al. 2017

IEEE Commun. Surv. Tutorials

Self Cite

417

200

View full text Add to dashboard Cite

show abstract

“…An energy-efficient UCB scheme (EE-UCB) is proposed in [23] and a test-case assuming a Manhattan-grid ultra-dense-network of small cells is considered. Each cell has two optional wireless last mile backhaul links: microwave (high resilience and low capacity) and millimetre wave (low resilience and high capacity).…”

Section: ) Energy Efficiencymentioning

confidence: 99%

On the joint optimisation of radio access and backhaul networks

Jaber

Imran

Tafazolli

et al. 2017

2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT)

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Abstract-The distinction between the radio access network and the backhaul network is disappearing with emerging advanced features. The backhaul is invading the radio assess spectrum to enable wireless in-band connections and the radio network is overlapping with the transport network through cloud processing and distributed functions. As such, it is of pivotal importance to approach the optimisation of the radio and transport networks jointly to avoid suboptimum operation and problem shifting. In this work, we present a critical analysis of the motivations, state-of-the-art advances, and potential benefits and challenges of joint RAN/backhaul optimisation.

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GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

Moin

Karim

Safdar

et al. 2018

Concurrency and Computation

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Network architectures nowadays are not able to address the requirements of future Internet, as they are not sufficient, ossified, and are power hungry on the TCP/IP paradigm. Therefore, to promote the realizable progression toward the new paradigms and modern protocols, modern routers are required to become programmable for the flexible control and dynamic management of both power states and traffic flows. In this scenario, Software Defined Networking (SDN) is considered as an exemplary approach for providing the support of dynamic traffic flows of each node on a network. Besides the benefits that SDN brought to the networking area, there are still crucial challenges such as to design an effective, lightweight API, rich algorithms, and traffic forwarding rules for SDN layers along with the adoption of an energy efficient infrastructure that could have the least effect on the environment. The aim of this research is to drive the attention toward environment friendly adoption and implementation of SDN that could deal with the growing number of users and network equipment. For this purpose, in this research, a comprehensive review on SDN while mapping the characteristics of green computing with SDN is provided. In addition to that, the research stressed on the benefits of Green SDN through a proposed G-SDN framework. Some well-defined case studies are presented to highlight the need of greening an SDN infrastructure. Finally, various research directions are presented to the wide adoption of SDN technology for academia and industry alike. KEYWORDS green computing, green SDN, SDN, software defined networks 1 INTRODUCTION Software Defined Networking (SDN) offers a high level of flexibility, controllability, and programmability in networks with the power of centralized control. The centralized controller at SDN faces a significant performance drawback when dealing with huge network flows. 1 On the other hand, features provided by SDN allows an ease in deploying the strategies that are designed to overcome the existing problems of power and energy-waste. 2 Green computing refers to the reduction of hazard and toxic materials, improved efficiency of energy including recycling of factory wastage, 3 and it has become an integral part to enhance the reliability of each emerging computing domains. Even the cloud and green computing are applicable side by side to make greener internet and enhance services. The former techniques allow the services with ease when required at a cost of complex infrastructure. However, the later approach has edge in providing all of these services with efficient energy usage, recyclability, low environmental impact, reduced carbon emission, and much more facilities. 4 In addition, to provide the promising services to the intended users, wired and wireless networks 5,6 have been followed by many directions in prior studies 5-7 such as the closing of forward and feedback loops. 8 In this way, there exists a variety of designs such as low-power solutions with slow and fast dynamics. Solutions with sl...

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Energy-Efficient SON-Based User-Centric Backhaul Scheme

Cited by 4 publications

References 8 publications

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks

On the joint optimisation of radio access and backhaul networks

GREEN SDN — An enhanced paradigm of SDN: Review, taxonomy, and future directions

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