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: 31 May 2020.

Special Issue Editor

Dr. Woongsup Lee
E-Mail 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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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (3 papers)

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Research

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
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
Open AccessArticle
CoRL: Collaborative Reinforcement Learning-Based MAC Protocol for IoT Networks
Electronics 2020, 9(1), 143; https://doi.org/10.3390/electronics9010143 (registering DOI) - 11 Jan 2020
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
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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