Abstract
The advent of the 5G mobile network has brought a lot of benefits. However, it prompted new challenges on the 5G network cybersecurity defense system, resource management, energy, cache, and mobile network, therefore making the existing approaches obsolete to tackle the new challenges. As a result of that, research studies were conducted to investigate deep learning approaches in solving problems in 5G network and 5G powered Internet of Vehicles (IoVs). In this article, we present a survey on the applications of deep learning algorithms for solving problems in 5G mobile network and 5G powered IoV. The survey pointed out the recent advances on the adoption of deep learning variants in solving the challenges of 5G mobile network and 5G powered IoV. The deep learning algorithm solutions for security, energy, resource management, 5G-enabled IoV, and mobile network in 5G communication systems were presented including several other applications. New comprehensive taxonomies were created, and new comprehensive taxonomies were suggested, analysed, and presented. The challenges of the approaches are already discussed in the literature, and new perspective for solving the challenges was outlined and discussed. We believed that this article can stimulate new interest in practical applications of deep learning in 5G network and provide clear direction for novel approaches to expert researchers.
Highlights
Previous Surveys and MotivationThere are a number of reviews on the applications of deep learning in 5G wireless mobile network
E 5G mobile wireless network aims to provide reliable connectivity ubiquitously [13]. e 5G wireless mobile network offers 1000 times increased in Internet traffic and is expected to give support to the industries and the Internet of ings technology. e 5G wireless mobile networks have more complications in design compared with the existing mobile communication technology and its diverse applications [14]. erefore, it requires advance artificial intelligent techniques to solve problems in the 5G wireless mobile networks
General Overview. e deep learning algorithms found to be frequently used in the 5G wireless network and 5Genabled Internet of Vehicles (IoVs) are as follows: generative adversarial network (GAN), deep reinforcement learning (DRL), convolutional neural network (CNN), long short-term memory (LSTM), deep recurrent neural network (DRNN), DDNN, and hybrid of the deep learning algorithm
Summary
There are a number of reviews on the applications of deep learning in 5G wireless mobile network. Aldweesh et al [21] conducted a survey on the applications of deep learning algorithms in detecting anomaly It mainly focused on the cyber security defense system for the 5G wireless mobile network. In view of the limitations in the previous surveys conducted, this paper proposes a comprehensive taxonomy showing different deep learning architectures and tasks in 5G wireless mobile networks. Adoption of deep learning architectures in performing different tasks in 5G wireless mobile networks is proposed. E deep learning architectures found to be applied in the 5G wireless mobile networks include CNN, DDNN, AE, GAN, LSTM, DRNN, hybrid deep learning, and DRL. Luo et al [69] proposed combination of CNN and deep Q-learning (CNN–DQL) scheme for dynamic transmission power control to improve the performance of the non-line-of-sight transmission in 5G network. It was found that the federated learning scheme preserved privacy and has high accuracy as well as effective communication cost (see Kong et al [110])
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