Cooperative channels allocation in unlicensed spectrum for D2D assisted 5G cellular network

Maksymyuk, Taras; Brych, Mykola; Klymash, Mykhailo; Jo, Minho

doi:10.1109/aiact.2017.8020098

Cited by 25 publications

(4 citation statements)

References 9 publications

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“…Cooperation of LTE-U cells was studied in [15] using a coalition formation game framework which is based on the well known Shapley value. A new approach was developed in [16] to exploit D2D capabilities of mobile devices for cooperative channel allocation in contention-based environment. The existing literatures mainly focus on how to guarantee fair coexistence, mitigate interference and allocate resources by using the cooperative mechanism.…”

Section: Introductionmentioning

confidence: 99%

Cooperative LBT Design and Effective Capacity Analysis for 5G NR Ultra Dense Networks in Unlicensed Spectrum

et al. 2019

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With the rapidly increasing demand of data traffic in fifth-generation (5G) wireless systems, various sophisticated techniques have been developed to cope with the demand. New radio-unlicensed (NR-U) technology is one of the most promising techniques to address the exponential growth of data traffic. At the same time, NR base stations are densely deployed in 5G. Therefore, large numbers of NR-U base stations attempt to access the unlicensed spectrum. Although listen before talk (LBT) with random backoff can guarantee fairness, it can also increase the collision probability. For the IEEE 802.11 Wi-Fi networks, a contention-based random access protocol is adopted, which cannot guarantee the Quality of Service (QoS).To overcome these problems, a new LBT protocol, referred to as cooperative LBT, is designed, in which zero forcing (ZF) precoding is applied to suppress the multi-user interference. Based on the new LBT protocol, a (N + 3)-state semi-Markovian model is established to characterize the effective capacity of NR-U in unlicensed bands, where N is the number of NR-U base stations. An expression for the effective capacity is obtained, which is a function of QoS, instantaneous transmit rate, the number of Wi-Fi nodes, and NR-U base stations. The influences of the finite backhaul and transmit power on the effective capacity are also analyzed in this paper. The simulation results show that the usage of cooperative communication can reduce the collision probabilities from 0.4928 to 0.1632 and increase the effective capacities by 220.04%. The cooperative LBT protocol has a more compelling advantage for 5G NR ultradense deployment scenarios.INDEX TERMS Effective capacity, new radio-unlicensed (NR-U), cooperative network, quality of service (QoS), listen before talk (LBT).

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Section: Introductionmentioning

confidence: 99%

Cooperative LBT Design and Effective Capacity Analysis for 5G NR Ultra Dense Networks in Unlicensed Spectrum

et al. 2019

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“…A traditional mobile network is a communication network where the link to and from end nodes is wireless, and the network is distributed over land areas called "cells", each served by at least one fixed-location transceiver. Currently, it is getting harder to offer the necessary quality of service (QoS) criteria [1][2][3], especially for users with high mobility, due to the state of technology and the volume of traffic consumed by users [4]. Mobility management is one of the major functions of network communication in traditional cellular networks or terrestrial networks (TN) and non-terrestrial networks (NTN), and mobility management aims to track where the users are, allowing calls and other user services to be delivered to them.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks

2023

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The non-terrestrial network (NTN) is a network that uses radio frequency (RF) resources mounted on satellites and includes satellite-based communications networks, high altitude platform systems (HAPS), and air-to-ground networks. The fifth generation (5G) and NTN may be crucial in utilizing communication infrastructure to provide 5G services in the future, anytime and anywhere. Based on the outcome of the Rel-16 study, the 3rd generation partnership project (3GPP) decided to start a work item on an NTN in 5G new radio (NR) Rel-17, and the focus of the study was on mobility management procedures, due to the movements of NTN platforms; especially, low earth orbit (LEO) satellites. Handover enhancements were discussed to tackle the frequent handover due to the fast satellite movement. Therefore, the major problem of handover in LEO satellite systems was the signaling storm created by handing over all user equipment (UE) in a cell to a new cell because when the UE crosses the boundary between the neighboring cell of a satellite, an intra-satellite or cell handover occurs; thus, all users in a cell are expected to experience a change of cell due to handover every few seconds. In addition, UE location is not easy to define due to moving cell/beam situations. In this study, we propose machine learning-based solutions for handover decisions in non-terrestrial networks for cell handovers or intra-satellite handovers to reduce signaling storms during handovers where the handover requests will be executed by clustered users. First, the dataset was generated by the simulator that simulates communication between users and satellites. Second, we preprocessed the data, and also used the feature creation technique to create the distance feature using the Haversine formula, and then applied clustering and classification algorithms. The experimental results show that the distance between a user and its cell center is an important parameter for handover decisions in NTN, and the random forest outperforms all models with a higher accuracy of 99% along with a better F1-score of 0.9961.

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“…Nevertheless, with the widespread deployment of 5G networks, 10-Gbps data rates can be expected for single small cells, which indicates data rates of several terabits per second via the transport network [6][7][8]. At these data rates, even the most effective DPI solutions cannot be effectively scaled to provide the required tradeoff between service quality and security.…”

Section: Introductionmentioning

confidence: 99%

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

Lutsiv¹,

Maksymyuk²,

Beshley³

et al. 2022

Computers, Materials &Amp; Continua

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The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels. With the massive number of connected devices, opportunities for potential network attacks are nearly unlimited. An additional problem is that many low-cost devices are not equipped with effective security protection so that they are easily hacked and applied within a network of bots (botnet) to perform distributed denial of service (DDoS) attacks. In this paper, we propose a novel intrusion detection system (IDS) based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems. The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies. An additional feature of the proposed IDS is that it is trained with an optimized dataset, where the number of features is reduced by 94% without classification accuracy loss. Thus, the proposed IDS remains stable in response to slight system perturbations, which do not represent network anomalies. The proposed approach is evaluated under different simulation scenarios and provides a 99% detection accuracy over known datasets while reducing the training time by an order of magnitude.

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Cooperative channels allocation in unlicensed spectrum for D2D assisted 5G cellular network

Cited by 25 publications

References 9 publications

Cooperative LBT Design and Effective Capacity Analysis for 5G NR Ultra Dense Networks in Unlicensed Spectrum

Cooperative LBT Design and Effective Capacity Analysis for 5G NR Ultra Dense Networks in Unlicensed Spectrum

Machine Learning-Based Solutions for Handover Decisions in Non-Terrestrial Networks

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

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