5G multi-tier radio access network planning based on voronoi diagram

Su, Jun; Beshley, Mykola; Przystupa, Krzysztof; Кочан, Орест; Rusyn, Bohdan; Stanisławski, Rafał; Yaremko, Oleg; Majka, M.; Beshley, Halyna; Demydov, Ivan; Pyrih, Yuliia; Kahalo, Ihor

doi:10.1016/j.measurement.2022.110814

Cited by 26 publications

(10 citation statements)

References 28 publications

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“…Stochastic geometry can be a solution to this problem. According to the proposed architecture of partitioning the coverage area of layer two between the BSs, we propose carrying out using the Voronoi method [38]. The essence of the method is to partition the plane so that each partitioning area consists of a set of points, each of which is closer to a certain object than to another.…”

Section: Proposed Energy-efficient Qoe-driven Rrm Methods For 5g and ...mentioning

confidence: 99%

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

2022

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This research was supported by the Ukrainian project No. 0120U100674 ("Development of the novel decentralized mobile network based on blockchainarchitecture and artificial intelligence for 5G/6G development in Ukraine"); in part by the Ukrainian project No. 0120U102201 ("Development the methods and unified software-hardware means for the deployment of the energy-efficient intent-based multi-purpose information and communication networks"),

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Section: Proposed Energy-efficient Qoe-driven Rrm Methods For 5g and ...mentioning

confidence: 99%

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

2022

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“…The connectivity of any ad hoc network is highly dependent on the mobility model it follows. As per the previous study mentioned in [41][42][43], the random way mobility model is widely used to simulate the performance of any mobile ad hoc network such as [44][45][46], fixing the source and sink nodes at the bottom and surface of the water, respectively, with coordinates (80, 90,100) and (10,20,30). To calculate the value of energy consumption, we use the 'Tx' Power 'Rx' Power and 0.007 W, respectively.…”

Section: Simulation Environmentmentioning

confidence: 99%

Trust-Based Beacon Node Localization Algorithm for Underwater Networks by Exploiting Nature Inspired Meta-Heuristic Strategies

et al. 2022

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Conventional underwater technologies were not able to provide authentication and proper visualization of unexplored ocean areas to accommodate a wide range of applications. The aforesaid technologies face several challenges including decentralization, beacon node localization (for identification of nodes), authentication of Internet of Underwater Things (IoUTs) objects and unreliable beacon node communication between purpose oriented IoT-enabled networks. Recently, new technologies such as blockchain (BC) and the IoUTs have been used to reduce the issues but there are still some research gaps; for example, unreliable beacon messages for node acquisition have significant impacts on node identification and localization and many constrained node resources, etc. Further, the uncertainty of acoustic communication and the environment itself become problems when designing a trust-based framework for the IoUTs. In this research, a trust-based hybrid BC-enabled beacon node localization (THBNL) framework is proposed to employ a secure strategy for beacon node localization (BNL) to mine the underwater localized nodes via the hybrid blockchain enabled beacon node localization (HB2NL) algorithm. This framework helps to merge two disciplines; it is hybrid because it follows the nature and bio inspired meta heuristics algorithms for scheduling the beacon nodes. The performance of the proposed approach is also evaluated for different factors such as node losses, packet delivery ratios, residual and energy consumption and waiting time analysis, etc. These findings show that the work done so far has been successful in achieving the required goals while remaining within the system parameters.

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“…5G network architecture is heterogeneous, that is, includes macro and small cells. Hence, optimization problems concerning spectrum allocation, location of these cells and energy saving of those not serving users are studied, as in Su et al [13]. Many of these optimization techniques are based on big data generated by both user and network operator.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

et al. 2023

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This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions.

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5G multi-tier radio access network planning based on voronoi diagram

Cited by 26 publications

References 28 publications

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

Energy-Efficient QoE-Driven Radio Resource Management Method for 5G and Beyond Networks

Trust-Based Beacon Node Localization Algorithm for Underwater Networks by Exploiting Nature Inspired Meta-Heuristic Strategies

Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

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