Radio resource management: approaches and implementations from 4G to 5G and beyond

Akhtar, Tafseer; Tselios, Christos; Politis, Ilias

doi:10.1007/s11276-020-02479-w

Cited by 41 publications

(27 citation statements)

References 215 publications

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“…is framework works cooperatively but no focus on radio resources management. A comprehensive review presented in [28] radio resource management schemes proposed for LTE/LTE-A networks is still relevant. However, these schemes should be adaptive and futuristic that meets the user's demands.…”

Section: Related Workmentioning

confidence: 99%

Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

Kalsoom

Ahmad

Alroobaea

et al. 2021

Journal of Advanced Transportation

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Internet of Vehicles (IoV) is a complex system that consists of resource types such as vehicles, humans, and sensors. Although the Internet of Vehicles is complex, it improvises communication among vehicles on the roads. Quality of service (QoS) enabled the cooperative driving system (CDS) based on 5G technology, enabling vehicles to communicate and cooperate to improve road traffic efficiency. Due to the high vehicle density and limited resources (bandwidth) of current network infrastructure, sometimes a better channel that meets the requirements of cooperative driving is not available that causes network congestion, which directly influences the overall QoS of the CDS. To overcome this, we proposed a 5G network-based architecture for CDS that incorporates a D2D technology-based resource allocation scheme. The proposed network architecture and cooperative behavior-based scheme helps in improving QoS for CDS. We implemented our proposed scheme by incorporating the density-based scattered clustering algorithm with noise for vehicular clustering. The proposed scheme’s performance shows significant improvement in terms of throughput compared with existing D2D approaches.

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Section: Related Workmentioning

confidence: 99%

Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

Kalsoom

Ahmad

Alroobaea

et al. 2021

Journal of Advanced Transportation

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“…, is the Head of Line (HOL) packet delay, whereas is a variable which characterizes each flow i. It is calculated from the ratio between the packet loss probability and the delay threshold (see (7)) [35].…”

Section: Modified Largest Weighted Delay First (Mlwdf)mentioning

confidence: 99%

“…RRM could be considered as an essential key factor for satisfying the requirements of the 5G environment, in terms of efficient management of available spectrum and harmful interference, in addition to fairness and Quality of Service (QoS) requirements [7].…”

Section: Introductionmentioning

confidence: 99%

Dynamic resource management algorithms for interference prediction in 5G new radio scenarios

Angri¹,

Mahfoudi²,

Najid³

2021

Preprint

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Efficient Radio Resource Management is a key mechanism in interference management in 5G New Radio (NR) networks, specifically in the case of the presence of mobile users moving at high speed. To this end, the prediction and the evaluation of the propagation channel sensitivity requires that the radio resources allocation in NR must be efficient and powerful. Therefore, several scheduling algorithms have been developed and tested using the mmWave model of NS-3 simulator, with the aim of enhancing their contribution to improving the quality of the signal received by users. The performances have been evaluated in terms of Signal-to-Interference-and-Noise-Ratio (SINR) and signal Block Error Rate (BLER). The simulations were run for different types of data flows, and achieved satisfactory results for most schemes. The achievements clearly show the importance of scheduling algorithms in lowering received interference, but they have also demonstrated the stability and reliability of some of those strategies.

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“…In the CART algorithm, the concept of information first is not continued but replaced by the GINI value [25]. If there is a data set S, the actual set of categories to which the data set belongs is L (N): Wireless Communications and Mobile Computing CNN consists of a series of convolutional layers, pooling layers, and fully connected layers, of which the convolutional layer is the core of CNN [26].…”

Section: Research On Modulation Recognitionmentioning

confidence: 99%

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

Huang

et al. 2021

Wireless Communications and Mobile Computing

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This paper implements a deep learning-based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time-frequency analysis methods, a time-frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time-frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.

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Radio resource management: approaches and implementations from 4G to 5G and beyond

Cited by 41 publications

References 215 publications

Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

Architecture for Resource Allocation in the Internet of Vehicles for Cooperating Driving System

Dynamic resource management algorithms for interference prediction in 5G new radio scenarios

Automatic Recognition of Communication Signal Modulation Based on the Multiple‐Parallel Complex Convolutional Neural Network

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