Performance Analysis for User-Centric Dense Networks With mmWave

Shi, Jianfeng; Pan, Cunhua; Zhang, Wence; Chen, Ming

doi:10.1109/access.2019.2893403

Cited by 19 publications

(3 citation statements)

References 29 publications

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“…Thus, shutting off such Aps, or minimizing their transmission power is a preferable strategy to minimize the impact of interference and total power consumption. For example, discussed three sleeping modes: cell-driven, core network-driven, and UE-driven [123,124]. The SC is triggered in the first mode if a planned active user is present.…”

Section: Interference In Udnsmentioning

confidence: 99%

Interference Challenges and Management in B5G Network Design: A Comprehensive Review

et al. 2022

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Beyond Fifth Generation (B5G) networks are expected to be the most efficient cellular wireless networks with greater capacity, lower latency, and higher speed than the current networks. Key enabling technologies, such as millimeter-wave (mm-wave), beamforming, Massive Multiple-Input Multiple-Output (M-MIMO), Device-to-Device (D2D), Relay Node (RN), and Heterogeneous Networks (HetNets) are essential to enable the new network to keep growing. In the forthcoming wireless networks with massive random deployment, frequency re-use strategies and multiple low power nodes, severe interference issues will impact the system. Consequently, interference management represents the main challenge for future wireless networks, commonly referred to as B5G. This paper provides an overview of the interference issues relating to the B5G networks from the perspective of HetNets, D2D, Ultra-Dense Networks (UDNs), and Unmanned Aerial Vehicles (UAVs). Furthermore, the existing interference mitigation techniques are discussed by reviewing the latest relevant studies with a focus on their methods, advantages, limitations, and future directions. Moreover, the open issues and future directions to reduce the effects of interference are also presented. The findings of this work can act as a guide to better understand the current and developing methodologies to mitigate the interference issues in B5G networks.

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Section: Interference In Udnsmentioning

confidence: 99%

Interference Challenges and Management in B5G Network Design: A Comprehensive Review

et al. 2022

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“…Note that, such assumption is mainly done for tractability. Its accuracy was validated in[59] by the simulation results and has provided the useful performance and observations. Hence, the validation is omitted for brevity in this paper.…”

mentioning

confidence: 93%

User-Centric Cooperative Transmissions-enabled Handover for Ultra-Dense Networks

Kibinda¹,

Ge²

2022

Preprint

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The user-centric cooperative transmission provides a compelling way to alleviate frequent handovers caused by an ever-increasing number of randomly deployed base stations (BSs) in ultra-dense networks (UDNs). This paper proposes a new user-centric cooperative transmissions-based handover scheme, i.e., the group-cell handover (GCHO) scheme, with the aim of reducing the handover rate in UDNs. In the proposed scheme, the boundary of the cooperating cluster depends on the distance among the user equipment (UE) and cooperating BSs. The new scheme captures the dynamicity and irregularity of the cooperating cluster topology resulting from randomly distributed BSs. Based on stochastic-geometry tools where BSs locations are modeled as the Poison point process (PPP), we derive an analytical expression of the handover rate for the UE with an arbitrary movement trajectory. Furthermore, a GCHO skipping (GCHO-S) scheme is proposed to minimize the handover cost, i.e., the percentage of time wasted in handover signaling in user-centric cooperative transmissions scenarios. The numerical results show that the GCHO scheme decreases the handover rate by 42.3% and 72.7% compared with the traditional single BS association and fixed-region cooperative network topology handover approaches, respectively. Moreover, under the same group-cell size and constant velocity, the GCHO-S scheme diminishes the handover cost by 50% against the GCHO scheme.

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“…The trends show that an increase in mobile data traffic will exceed zettabyte (200 10 ) by 2025 [2]. The total network traffic pattern has increased exponentially the last few years [3,4]. Furthermore, it has also been reported that such rapid growth in data traffic is likely to cause security breaches, risks, and lowering of network performance, especially in communication network systems [5,6].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of Ensemble Learning and Supervised Algorithms in Classifying Multi-label Network Traffic Flow

Machoke

Mbelwa

Agbinya

et al. 2022

Eng. Technol. Appl. Sci. Res.

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Network traffic classification is of significant importance. It helps identify network anomalies and assists in taking measures to avoid them. However, classifying network traffic correctly is a challenging task. This study aims to compare ensemble learning methods with normal supervised classification to come up with improved classification methods. Three types of network traffic were classified (Benign, Malicious, and Outliers). The data were collected experimentally by using Paessler Router Traffic Grapher software and online and were analyzed by R software. The datasets were used to train five supervised models (k-nearest neighbors, mixture discriminant analysis, Naïve Bayes, C5.0 classification model, and regularized discriminant analysis). The models were trained by 70% of the samples and the rest 30% were used for validation. The same samples were used separately in predicting individual accuracy. The results were compared to the ensemble learning models which were built with the use of the same datasets. Among the five supervised classifiers, k-nearest neighbors and C5.0 classification scored the highest accuracy of 0.868 and 0.761. The ensemble learning classifiers Bagging (Random Forest) and Boosting (eXtreme Gradient Boosting) had accuracy of 0.904 and 0.902 respectively. The results show that the ensemble learning method has higher accuracy compared to the normal supervised classifiers. Therefore, it can be used to detect malicious activities in network traffic as well as anomalies with improved accuracy.

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Performance Analysis for User-Centric Dense Networks With mmWave

Cited by 19 publications

References 29 publications

Interference Challenges and Management in B5G Network Design: A Comprehensive Review

Interference Challenges and Management in B5G Network Design: A Comprehensive Review

User-Centric Cooperative Transmissions-enabled Handover for Ultra-Dense Networks

Performance Comparison of Ensemble Learning and Supervised Algorithms in Classifying Multi-label Network Traffic Flow

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