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Electronics
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Applications of AI in Wireless Communication
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Applications of AI in Wireless Communication

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 7104

Special Issue Editor

Dr. Hassan Malik

E-Mail Website
Guest Editor
Department of Computer Science, Edge Hill University, Lancashire L39 4QP, UK
Interests: electrical engineering; telecommunications engineering; communication engineering; wireless communications; internet of things; signal processing; embedded software

Special Issue Information

Dear Colleagues,

Wireless networks are rich in data, where data are continuously gathered from a massive amount of user devices and network entities in the form of radio and system measurements. However, the traditional communication protocols considered such data as a short-lived localized commodity due to ageng and user mobility. Hence, drives little insight from such data and results in reactive systems. Recently, the exploration of AI and learning techniques in the field of wireless communication has gained interest and researchers are exploring different techniques to shift the traditional reactive wireless networks to proactive networks. The application of AI in wireless networks provides an opportunity to exploit wireless data in multiple dimensions and create data-driven wireless networks that are more robust and autonomous in changing environments.

This Special Issue encourages the submission of high-quality, innovative, and original contributions of papers dealing with the application of AI in wireless networks. Submission is expected to focus on different verticals of wireless network and future 6G use-case scenarios and propose new solutions, experiment or review current trends, discuss the potential open challenges, etc.

Potential topics include, but are not limited to:

  • AI for Physical and MAC layer protocols
  • Application of AI in Radio Resource and Interference Management
  • AI for Localization Techniques in Wireless Networks
  • Application of AI for Wireless Security and Privacy
  • AI techniques for Cellular LPWAN
  • AI techniques for multi-interface wireless network
  • The application of AI in Connected Vehicles, smart healthcare, and smart cities uses cases from the perspective of wireless communication.
  • Self-organizing networks
  • Trails, testbed, and experiments

Dr. Hassan Malik
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 semimonthly 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 2400 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.

Published Papers (3 papers)

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Research

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15 pages, 537 KiB  
Article
Spectrum Sensing Algorithm Based on Self-Supervised Contrast Learning
by Xinyu Li, Zhijin Zhao, Yupei Zhang, Shilian Zheng and Shaogang Dai
Electronics 2023, 12(6), 1317; https://doi.org/10.3390/electronics12061317 - 9 Mar 2023
Cited by 2 | Viewed by 1136
Abstract
The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in order to solve this problem, [...] Read more.
The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in order to solve this problem, and a spectrum sensing algorithm based on self-supervised contrast learning (SSCL) is proposed. The algorithm mainly includes two stages: pre-training and fine-tuning. In the pre-training stage, according to the characteristics of communication signals, data augmentation methods are designed to obtain the pre-trained positive sample pairs, and the features of the positive sample pairs of unlabeled samples are extracted by self-supervised contrast learning to obtain the feature extractor. In the fine-tuning stage, the parameters of the feature extraction layer are frozen, and a small number of labeled samples are used to update the parameters of the classification layer, and the features and labels are connected to get the spectrum sensing classifier. The simulation results demonstrate that the SSCL algorithm has better detection performance over the semi-supervised algorithm and the traditional energy detection algorithm. When the number of labeled samples used is only 10% of the supervised algorithm and the SNR is higher than −12 dB, the detection probability of the SSCL algorithm is higher than 97%, which is slightly lower than the supervised algorithm. Full article
(This article belongs to the Special Issue Applications of AI in Wireless Communication)
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13 pages, 3278 KiB  
Article
Path Loss Prediction in Tropical Regions using Machine Learning Techniques: A Case Study
by Oluwole John Famoriji and Thokozani Shongwe
Electronics 2022, 11(17), 2711; https://doi.org/10.3390/electronics11172711 - 29 Aug 2022
Cited by 5 | Viewed by 1349
Abstract
In optimization of wireless networks, path loss prediction is of great importance for adequate planning and budgeting in wireless communications. For efficient and reliable communications in the tropics, determination or estimation of channel parameters becomes important. Research for this article employed different machine [...] Read more.
In optimization of wireless networks, path loss prediction is of great importance for adequate planning and budgeting in wireless communications. For efficient and reliable communications in the tropics, determination or estimation of channel parameters becomes important. Research for this article employed different machine learning techniques—AdaBoost, support vector regression (SVR), and back propagation neural networks (BPNNs)—to construct path loss models for Akure metropolis, Ondo state, Nigeria. An experimental measurement campaign was conducted for three different broadcasting stations (Ondo State Radiovision Corporation (OSRC), Orange FM, and FUTA FM) all situated within Akure metropolis. Furthermore, we designed machine learning-based models for path loss prediction at various observation points at a particular frequency, and demonstrated how these algorithms agree with the measured data. For instance, for OSRC (operating at 96.5 MHz) measurement, the RMSEs (root mean square errors) of AdaBoost, SVR, BPNN, and the classical model (log-distance model) predictors were 4.15 dB, 6.22 dB, 6.75 dB, and 1.41 dB, respectively. Additionally, path loss prediction at a new frequency according to the available data at specific frequencies was evaluated. In order to resolve the challenge of limited or insufficient samples at a new frequency, a framework hybridizing classical models and machine learning algorithms was developed. The developed framework employs estimated values that are computed by the classical model based on the prior information for the training set expansion. Performance evaluation of the framework was conducted using measured data of Orange FM (94.5 MHz) and FUTA FM (93.1 MHz), and the samples computed from the classical model were used as training datasets for path loss prediction at a new frequency. RMSEs of AdaBoost, SVR, BPNN, and log-distance predictors were 1.77 dB, 1.52 dB, 1.45 dB, and 2.61 dB, respectively. However, adding measured data generated by the classical-based model, the RMSEs of AdaBoost, SVR, BPNN, and log-distance algorithms were 1.81 dB, 1.63 dB, 1.45 dB, and 1.88 dB, respectively. The results demonstrate how the proposed sample expansion framework enhances prediction performance in the scenario of few measured data at a new frequency. Finally, these results are promising enough for the deployment of the proposed technique in practical scenarios. Full article
(This article belongs to the Special Issue Applications of AI in Wireless Communication)
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Review

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18 pages, 519 KiB  
Review
A Detailed Survey on Federated Learning Attacks and Defenses
by Hira Shahzadi Sikandar, Huda Waheed, Sibgha Tahir, Saif U. R. Malik and Waqas Rafique
Electronics 2023, 12(2), 260; https://doi.org/10.3390/electronics12020260 - 4 Jan 2023
Cited by 9 | Viewed by 3734
Abstract
A traditional centralized method of training AI models has been put to the test by the emergence of data stores and public privacy concerns. To overcome these issues, the federated learning (FL) approach was introduced. FL employs a privacy-by-design architecture to train deep [...] Read more.
A traditional centralized method of training AI models has been put to the test by the emergence of data stores and public privacy concerns. To overcome these issues, the federated learning (FL) approach was introduced. FL employs a privacy-by-design architecture to train deep neural networks utilizing decentralized data, in which numerous devices collectively build any machine learning system that does not reveal users’ personal information under the supervision of a centralized server. While federated learning (FL), as a machine learning (ML) strategy, may be effective for safeguarding the confidentiality of local data, it is also vulnerable to attacks. Increased interest in the FL domain inspired us to write this paper, which informs readers of the numerous threats to and flaws in the federated learning strategy, and introduces a multiple-defense mechanism that can be employed to fend off threats. Full article
(This article belongs to the Special Issue Applications of AI in Wireless Communication)
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