A review on resource allocation techniques in D2D communication for 5G and B5G technology

Jayakumar, S.; Nandakumar, S.

doi:10.1007/s12083-020-00962-x

Cited by 55 publications

(25 citation statements)

References 86 publications

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“…The maximum likelihood hypothesis testing method based on decision theory theoretically ensures that its decision results are optimal under the Bayesian least-cost criterion and can guarantee the performance of the method in a certain low signal-to-noise environment. However, the main drawbacks of the method are as follows: more a priori knowledge is required, such as signal-to-noise ratio, carrier frequency, symbol rate, oversampling multiplier, and other parameters, and secondly, the existence of unknown parameters leads to a complex computational push-to process and high computational complexity, which is difficult to implement in practical production [5]. The classification function is relatively single, and all common modulation types cannot be identified by one framework; in the actual channel, the noise is not necessarily Gaussian white noise and there are multipath fading, time delay, Doppler effects, and other effects [6].…”

Section: Related Workmentioning

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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Section: Related Workmentioning

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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“…In [60], the author discussed the various algorithms related to resource allocation and deeply analyzed the methodologies for the evaluation founded on the involvement of Base Station for the sake of research scope and to offer theoretical support to the problems associated with resource allocation having the environment of D2D communication.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation Schemes For 5G Network: A Systematic Review

Kamal¹,

Raza²,

Alam³

et al. 2021

Preprint

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Fifth Generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising of device-to-device networks, macro-cells, and dissimilar categories of small-cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need and the lack of resources, the resource distribution problem desires to be focused capably to accomplish the traffic and to enhance network working. One of the utmost serious problems is to alleviate the jamming from the network in support of having a better QoS. However, there are limited review papers written on resource distribution, there is no particularize and organized review carry out in 5G resource allocation. Hence, this article covers and evaluates the argument using a classification of existing developing resource allocation schemes in 5G thoroughly by classifying the schemes to enhance the service quality. This survey comprises the discussion based on metrics used to evaluate the performance. It would also permit ahead beyond evidence on resource allocation methods in 5G and empowers the scholars to meet the present research areas to focus on.

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“…The proposed methods enhance the lifetime of direct communication under cellular network. Jayakumar et al [9] described the utility of device-to-device communication in next-generation wireless communication. The direct and sharing approach of D2D communication improves the quality of service of 5G networks.…”

Section: Related Workmentioning

confidence: 99%

“…The impact of intracell interference is very high and decreases the performance of networks [6][7][8]. Various authors and research scholars suggest that resource optimization is a better way to handle resource selection in sharing mode such as power control [9,10]. Various authors reported that the sharing of resources with a cellular network is a single-way communication.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Resource and Energy Utilization in Device-to-Device Communication Under Cellular Network

Jatav

Datar²,

Malviya³

2021

Intelligent Sustainable Systems

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The proposed algorithm uses the particle swarm optimization algorithm. The applied particle swarm intelligence defines dual constraints function for the resource allocation in shared environments. The optimization process reduces the transmit power of D2D users and cellular users and increases the efficiency of the communication mode. The proposed algorithm describes the way of communication in the dedicated system model of the D2D communication interface. The proposed algorithm reduces the inference links and enhances the quality of service for the communication system. The incorporation of particle swarm optimization algorithm maintains the equilibrium state in both users. The proposed algorithm simulates in MATLAB environments and estimates the standard parameters. The analysis of results indicates that the proposed algorithm is better than the existing algorithms of D2D communication.

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A review on resource allocation techniques in D2D communication for 5G and B5G technology

Cited by 55 publications

References 86 publications

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

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

Resource Allocation Schemes For 5G Network: A Systematic Review

Optimization of Resource and Energy Utilization in Device-to-Device Communication Under Cellular Network

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