A survey on green communication and security challenges in 5G wireless communication networks

Gandotra, Pimmy; Jha, Rakesh Kumar

doi:10.1016/j.jnca.2017.07.002

Cited by 83 publications

(36 citation statements)

References 160 publications

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“…Moreover, the operating expenses of mobile network infrastructures, such as energy consumption and maintenance spending, are substantially increasing [4]. Therefore, optimizing the capital expenditures and operating expenses has become a necessity for mobile network operators [5,6].…”

Section: Introductionmentioning

confidence: 99%

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Chen

Yang

Zhang

et al. 2018

Journal of Network and Computer Applications

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The increasingly growing data traffic has posed great challenges for mobile operators to increase their data processing capacity, which incurs a significant energy consumption and deployment cost. With the emergence of the Cloud Radio Access Network (C-RAN) architecture, the data processing units can now be centralized in data centers and shared among base stations. By mapping a cluster of base stations with complementary traffic patterns to a data processing unit, the processing unit can be fully utilized in different periods of time, and the required capacity to be deployed is expected to be smaller than the sum of capacities of single base stations. However, since the traffic patterns of base stations are highly dynamic in different time and locations, it is challenging to foresee and characterize the traffic patterns in advance to make optimal clustering schemes. In this paper, we address these issues by proposing a deep-learning-based C-RAN optimization framework. First, we exploit a Multivariate Long Short-Term Memory (MuLSTM) model to learn the temporal dependency and spatial correlation among base station traffic patterns, and make accurate traffic forecast for a future period of time. Afterwards, we build a weighted graph to model the complementarity of base stations according to their traffic patterns, and propose a Distance-Constrained Complementarity-Aware (DCCA) algorithm to find optimal base station clustering schemes with the objectives of optimizing capacity utility and deployment cost. We evaluate the performance of our framework using data in two months from real-world mobile networks in Milan and Trentino, Italy. Results show that our method effectively increases the average capacity utility to 83.4% and 76.7%, and reduces the overall deployment cost to 48.4% and 51.7% of the traditional RAN architecture in the two datasets, respectively, which consistently outperforms the state-of-the-art baseline methods.

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Section: Introductionmentioning

confidence: 99%

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Chen

Yang

Zhang

et al. 2018

Journal of Network and Computer Applications

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“…Minimizing energy usage in this particular planet is, therefore, a primary goal of NGNs tools. Sustainability as well as price reduction specifications are approached with equal value of the development of environmentally friendly media strategies, both a consequence of the more effective operation [8], [10]. In enhancing the effectiveness with which communications happen, price per transmission will go down and sustainability enhanced.…”

Section: B Goals Of Green Networkingmentioning

confidence: 99%

“…For example, in the context of environmentally friendly correspondence through cognitive radio, writers mention that you will find two essential but entangled aspects: how you can make use of cognitive stereo for energy efficiency purposes, and how you can create the cognitive radio work in an energy efficient fashion. Escalating electricity costs and environmental worries have produced an immediate need for more energy-efficient "green" wireless communication [2], [8]. Hence, we have to be assertive in developing energy efficient ways for cooperative and also cognitive networks that will likely operate the upcoming generation of wireless communication.…”

Section: Design (Addressing Energy Efficiency In Future Generation Wimentioning

confidence: 99%

Appreciating Green Radio Communication Network Systems in a Problem-Oriented Project-Based Learning Environment. A Theoretical Framework

Akor¹,

Subari²,

Jambari³

et al. 2019

IJEAT

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The aim of this article was to present a POPBL theoretical framework for the teaching, learning and research in Green Radio Communication Network Systems. To this effect, a content analysis and synthesis of related scientific literature, models and frameworks was carried out based on the research tittle and key words. The article analyses and syntheses centred on the importance of green radio communication, principal factors in green communication as well as the process components. Also in the content analysis and synthesis are the POPBL elements, processes and related theories with the resulting instructional benefits. The resulting framework has the potentials of developing the generic skills like problem solving, team work, creativity, critical thinking and continues learning which have the potentials of achieving the goals of GRCNS.

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“…The rising traffic volumes and the growing technologies to support them is accompanied by a rise in the cost of energy and the carbon dioxide (CO 2 ) footprint in the atmosphere. This has led to the emergence of green communication in 5G WCNs . Energy efficiency (EE) in WCNs has thus been identified as a key apprehension bracing green communication.…”

Section: Introductionmentioning

confidence: 99%

E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

Gandotra

Jha

Jain

2018

Trans Emerging Tel Tech

Self Cite

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The massive spread of the smart devices demanding high data rate and delay‐sensitized applications has escalated energy consumption levels in the fifth‐generation (5G) wireless communication networks (WCNs). It thus becomes imperative for the mobile network operators to meet the tremendous subscriber demands using energy‐efficient techniques. The limited battery capacities of the mobile terminals (MTs) further make energy efficiency (EE) critical. Device‐to‐device (D2D) communication ensues to be a key enabler technology in the realization of an energy‐efficient 5G WCN. It also serves as a paramount technology for green communication. This paper studies a resource allocation scheme, which iteratively accentuates adaptive reuse of resource blocks (RBs) from the active cellular users to the D2D pairs in a tri‐sectored cell. The cellular users involved in RB allocation to pairs are termed as active. Required count of RBs for each D2D pair is chosen by employing the hidden Markov model. The RB reuse strategy favors enhancement in throughput. The proposed energy‐efficient adaptive RB allocation with low complexity (E2ARC) scheme aims to guarantee ample RBs to individual D2D pairs to optimize the power consumption levels. With advanced throughput and optimal power, augmentation in EE is achieved with a high degree of fairness. E2ARC also comprehends low complexity. The efficacy of the proposed scheme is demonstrated by the simulation results.

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A survey on green communication and security challenges in 5G wireless communication networks

Cited by 83 publications

References 160 publications

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Deep mobile traffic forecast and complementary base station clustering for C-RAN optimization

Appreciating Green Radio Communication Network Systems in a Problem-Oriented Project-Based Learning Environment. A Theoretical Framework

E2ARC: Energy‐efficient adaptive resource block allocation with low complexity in device‐to‐device communication

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