Self-optimised joint traffic offloading in heterogeneous cellular networks

Xu, Lexi; Luan, Yuting; Cheng, Xinzhou; Xing, Huanlai; Liu, Yu; Jiang, Xiangui; Chen, Weiwei; Chao, Kun

doi:10.1109/iscit.2016.7751633

Cited by 52 publications

(2 citation statements)

References 17 publications

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“…Furthermore, simulation results have shown that the proposed algorithm assures the computation of all the applications, subject to both latency and battery lifetime constraints. Moreover, L. Xu et al [7] have proposed a self-optimized joint traffic offloading (JTO) scheme to offload traffic between picocells and macrocells and achieve mobility load balancing among macrocells. Through computer simulations, it has been proved that the JTO scheme can effectively deal with the cell exacerbation problem regarding handover failure and call dropping.…”

Section: Introductionmentioning

confidence: 99%

An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks

Tsipi

Karavolos

Vouyioukas

2022

Telecom

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Today’s terrestrial cellular communications networks face difficulties in serving coexisting users and devices due to the enormous demands of mass connectivity. Further, natural disasters and unexpected events lead to an unpredictable amount of data traffic, thus causing congestion to the network. In such cases, the addition of on-demand network entities, such as fixed or aerial base stations, has been proposed as a viable solution for managing high data traffic and offloading the existing terrestrial infrastructure. This paper presents an unmanned aerial vehicles (UAVs) aided offloading strategy of the terrestrial network, utilizing an unsupervised machine learning method for the best placement of UAVs in sites with high data traffic. The proposed scheme forms clusters of users located in the affected area using the k-medoid algorithm. Followingly, based on the number of available UAVs, a cluster selection scheme is employed to select the available UAVs that will be deployed to achieve maximum offloading in the system. Comparisons with traditional offloading strategies integrating terrestrial picocells and other UAV-aided schemes show that significant offloading, throughput, spectral efficiency, and sum rate gains can be harvested through the proposed method under a varying number of UAVs.

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Section: Introductionmentioning

confidence: 99%

An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks

Tsipi

Karavolos

Vouyioukas

2022

Telecom

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“…The network of providing resource is called 'Cloud'. The hardware resource in the 'Cloud' seems scalable infinitely and can be used whenever [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

The Realization Path of Network Security Technology Under Big Data and Cloud Computing

Kang

Cheng

Bingyi

et al. 2019

Lecture Notes in Electrical Engineering

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This paper studies the cloud and big data technology based on the characters of network security, including virus invasion, data storage, system vulnerabilities, network management etc. It analyzes some key network security problems in the current cloud and big data network. Above all, this paper puts forward technical ways of achieving network security. Cloud computing is a service that based on the increased usage and delivery of the internet related services, it promotes the rapidly development of the big data information processing technology, improves the processing and management abilities of big data information. With tie rapid development of computer technology, big data technology brings not only huge economic benefits, but the evolution of social productivity. However, serials of safety problems appeared. How to increase network security has been become the key point. This paper analyzes and discusses the technical ways of achieving network security.

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