Special Issue "Application of Artificial Intelligence and Deep Learning in Wireless Communications Systems"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 20 March 2021.

Special Issue Editor

Dr. Woongsup Lee
Website
Guest Editor
Department of Information and Communication Engineering, Institute of Marine Industry, Gyeongsang National University, Tongyeong 53064, Korea
Interests: Deep learning; Wireless communications; Cognitive radio; Smart grid; Data science

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed the rapid advance of wireless technologies, which includes fifth generation (5G) communication, fog networking, molecular communication, and millimeter wave communication. Compared to traditional wireless technologies, these new technologies will have diverse service requirements, e.g., extremely low delay, and complicated system models that are harder to properly manage with conventional approaches. Accordingly, a new research paradigm needs to be devised.

Recently, artificial intelligence (AI) and deep learning (DL) technology have gained a lot of popularity due to their remarkable performance compared to traditional schemes, and they have begun to be applied in wireless communications. In particular, these data-driven approaches have the capability to change the paradigm of research, from the sophisticated mathematical model-based approach to a learning-based approach where the scheme is designed autonomously observing large amounts of data. Given that the AI- and DL-based schemes are more adaptable to the wireless environment, do not rely on the mathematically tractable system model, and show lower computational complexity during run-time, they are more appropriate for the recent wireless technologies, i.e., future wireless communication systems.

This Special Issue encourages the submission of high-quality, innovative, and original contributions covering contributions regarding future wireless communication systems, especially the application of AI and DL in wireless communication systems.

The list of possible topics includes but is not limited to:

  • Challenges and design requirement for future wireless communication systems;
  • Application of DL and AI for wireless communication systems;
  • Evaluating the limitations of AI and DL in wireless communications;
  • Big data for future wireless communication systems;
  • Security for future wireless communication systems;
  • Implementation of DL and AI technology for wireless communication.

Dr. Woongsup Lee
Guest Editor

Manuscript Submission Information

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Keywords

  • machine learning
  • deep learning
  • artificial intelligence
  • future wireless communication systems

Published Papers (6 papers)

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Research

Open AccessArticle
A Survey on Machine Learning-Based Performance Improvement of Wireless Networks: PHY, MAC and Network Layer
Electronics 2021, 10(3), 318; https://doi.org/10.3390/electronics10030318 - 29 Jan 2021
Viewed by 387
Abstract
This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper [...] Read more.
This paper presents a systematic and comprehensive survey that reviews the latest research efforts focused on machine learning (ML) based performance improvement of wireless networks, while considering all layers of the protocol stack: PHY, MAC and network. First, the related work and paper contributions are discussed, followed by providing the necessary background on data-driven approaches and machine learning to help non-machine learning experts understand all discussed techniques. Then, a comprehensive review is presented on works employing ML-based approaches to optimize the wireless communication parameters settings to achieve improved network quality-of-service (QoS) and quality-of-experience (QoE). We first categorize these works into: radio analysis, MAC analysis and network prediction approaches, followed by subcategories within each. Finally, open challenges and broader perspectives are discussed. Full article
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Open AccessArticle
Deep Scanning—Beam Selection Based on Deep Reinforcement Learning in Massive MIMO Wireless Communication System
Electronics 2020, 9(11), 1844; https://doi.org/10.3390/electronics9111844 - 04 Nov 2020
Viewed by 421
Abstract
In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, [...] Read more.
In this paper, we investigate a deep learning based resource allocation scheme for massive multiple-input-multiple-output (MIMO) communication systems, where a base station (BS) with a large scale antenna array communicates with a user equipment (UE) using beamforming. In particular, we propose Deep Scanning, in which a near-optimal beamforming vector can be found based on deep Q-learning. Through simulations, we confirm that the optimal beam vector can be found with a high probability. We also show that the complexity required to find the optimum beam vector can be reduced significantly in comparison with conventional beam search schemes. Full article
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Open AccessArticle
Harnessing the Adversarial Perturbation to Enhance Security in the Autoencoder-Based Communication System
Electronics 2020, 9(2), 294; https://doi.org/10.3390/electronics9020294 - 08 Feb 2020
Viewed by 687
Abstract
Given the vulnerability of deep neural network to adversarial attacks, the application of deep learning in the wireless physical layer arouses comprehensive security concerns. In this paper, we consider an autoencoder-based communication system with a full-duplex (FD) legitimate receiver and an external eavesdropper. [...] Read more.
Given the vulnerability of deep neural network to adversarial attacks, the application of deep learning in the wireless physical layer arouses comprehensive security concerns. In this paper, we consider an autoencoder-based communication system with a full-duplex (FD) legitimate receiver and an external eavesdropper. It is assumed that the system is trained from end-to-end based on the concepts of autoencoder. The FD legitimate receiver transmits a well-designed adversary perturbation signal to jam the eavesdropper while receiving information simultaneously. To defend the self-perturbation from the loop-back channel, the legitimate receiver is re-trained with the adversarial training method. The simulation results show that with the scheme proposed in this paper, the block-error-rate (BLER) of the legitimate receiver almost remains unaffected while the BLER of the eavesdropper is increased by orders of magnitude. This ensures reliable and secure transmission between the transmitter and the legitimate receiver. Full article
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Open AccessArticle
Low-Cost Image Search System on Off-Line Situation
Electronics 2020, 9(1), 153; https://doi.org/10.3390/electronics9010153 - 14 Jan 2020
Cited by 1 | Viewed by 852
Abstract
Implementation of deep learning in low-cost hardware, such as an edge device, is challenging. Reducing the complexity of the network is one of the solutions to reduce resource usage in the system, which is needed by low-cost system implementation. In this study, we [...] Read more.
Implementation of deep learning in low-cost hardware, such as an edge device, is challenging. Reducing the complexity of the network is one of the solutions to reduce resource usage in the system, which is needed by low-cost system implementation. In this study, we use the general average pooling layer to replace the fully connected layers on the convolutional neural network (CNN) model, used in the previous study, to reduce the number of network properties without decreasing the model performance in developing image classification for image search tasks. We apply the cosine similarity to measure the characteristic similarity between the feature vector of image input and extracting feature vectors from testing images in the database. The result of the cosine similarity calculation will show the image as the result of the searching image task. In the implementation, we use Raspberry Pi 3 as a low-cost hardware and CIFAR-10 dataset for training and testing images. Base on the development and implementation, the accuracy of the model is 68%, and the system generates the result of the image search base on the characteristic similarity of the images. Full article
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Open AccessArticle
CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks
Electronics 2020, 9(1), 143; https://doi.org/10.3390/electronics9010143 - 11 Jan 2020
Cited by 3 | Viewed by 976
Abstract
Devices used in Internet of Things (IoT) networks continue to perform sensing, gathering, modifying, and forwarding data. Since IoT networks have a lot of participants, mitigating and reducing collisions among the participants becomes an essential requirement for the Medium Access Control (MAC) protocols [...] Read more.
Devices used in Internet of Things (IoT) networks continue to perform sensing, gathering, modifying, and forwarding data. Since IoT networks have a lot of participants, mitigating and reducing collisions among the participants becomes an essential requirement for the Medium Access Control (MAC) protocols to increase system performance. A collision occurs in wireless channel when two or more nodes try to access the channel at the same time. In this paper, a reinforcement learning-based MAC protocol was proposed to provide high throughput and alleviate the collision problem. A collaboratively predicted Q-value was proposed for nodes to update their value functions by using communications trial information of other nodes. Our proposed protocol was confirmed by intensive system level simulations that it can reduce convergence time in 34.1% compared to the conventional Q-learning-based MAC protocol. Full article
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Open AccessFeature PaperArticle
A Deep Learning Based Transmission Algorithm for Mobile Device-to-Device Networks
Electronics 2019, 8(11), 1361; https://doi.org/10.3390/electronics8111361 - 16 Nov 2019
Cited by 2 | Viewed by 808
Abstract
Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. [...] Read more.
Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power. Full article
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