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Advances and Applications of Machine Learning for Wireless Communications and Networking

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 15024

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Special Issue Editors

Dr. Jie Gao

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Guest Editor

School of Information Technology, Carleton University, Ottawa, ON, Canada
Interests: machine learning for communications and networking; Internet of Things (IoT) and Industrial IoT; connected and autonomous vehicles; protocol design for future wireless networks; cloud and mobile edge computing; multiagent systems and cooperative communications
Special Issues, Collections and Topics in MDPI journals

Dr. Yuan Wu

E-Mail Website
Guest Editor

Department of Computer and Information Science, University of Macau, Macao, China
Interests: edge/cloud computing and edge intelligence; green and cognitive communications; integrated sensing; communications; and computing; intelligent communications systems and networks; vehicular communications and networks; energy informatics
Special Issues, Collections and Topics in MDPI journals

Dr. Hina Tabassum

E-Mail Website
Guest Editor

Department of Electrical Engineering & Computer Science, York University, Toronto, ON, Canada
Interests: 5G/B5G wireless communications; massive MIMO; mm-wave; visible light communication; free space optics; stochastic geometry; geometric probability; smart grid; energy harvesting systems; ultra-reliable; low latency communication

Dr. Lian Zhao

E-Mail Website
Guest Editor

Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada
Interests: wireless communications, edge computing, caching and communications; centralized and distributed resource management; incentive mechanism design, optimizations; vehicular ad-hoc networks: safety message dissemination, SDN technologies; green communication and computation

Special Issue Information

Dear Colleagues,

To cope with the expanding variety of services and their increasingly stringent requirements, communication networks are becoming increasingly complex. Correspondingly, the conventional analytical or heuristic approaches in wireless communications and networking research are no longer sufficient. Considering sixth generation (6G) networks, artificial intelligence (AI) is envisioned as an indispensable part of communication networks, and machine learning for wireless communications and networking has attracted significant research interest. Empowered by machine learning, 6G networks are anticipated to become more adaptive, intelligent, energy-efficient, and scalable.

In this Special Issue, we focus on the advances and applications of machine learning for wireless communications and networking with regard to 6G. We are interested in articles that demonstrate new ideas and share new results on how to effectively use machine learning to empower wireless communications and manage communication networks toward the goal of AI-native networks. Specific machine-learning-based research topics of interest may include but are not limited to:

Network architecture innovations (e.g., AI-native networks);
Intelligent network resource management;
Machine-learning-assisted cloud and multi-access edge computing;
Edge intelligence and networked AI applications;
Machine-learning-based Internet of Things (IoT), Industrial Internet of Things (IIoT), and Internet of Vehicles (IoV);
Machine-learning-based approaches for heterogeneous networks (e.g., UAV or satellite networks);
Machine-learning-based network protocol design and analysis;
Spatiotemporal demand forecast in networks;
Machine-learning-based signal processing for communications;
Machine-learning-based physical-layer techniques (e.g., cell-free massive MIMO);
Machine-learning-based network security and privacy design and analysis.

If you want to learn more information or need any advice, you can contact the Special Issue Editor Penelope Wang via <[email protected]> directly.

Dr. Jie Gao
Dr. Yuan Wu
Dr. Hina Tabassum
Dr. Lian Zhao
Guest Editors

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. Sensors 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 2600 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.

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Published Papers (7 papers)

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Research

15 pages, 5243 KiB

Open AccessArticle

A Deep Learning-Based Emergency Alert Wake-Up Signal Detection Method for the UHD Broadcasting System

by Jin-Hyuk Song, Myung-Sun Baek, Byungjun Bae and Hyoung-Kyu Song

Sensors 2024, 24(13), 4162; https://doi.org/10.3390/s24134162 - 26 Jun 2024

Viewed by 1108

Abstract

With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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18 pages, 13734 KiB

Open AccessArticle

Channel-Blind Joint Source–Channel Coding for Wireless Image Transmission

by Hongjie Yuan, Weizhang Xu, Yuhuan Wang and Xingxing Wang

Sensors 2024, 24(12), 4005; https://doi.org/10.3390/s24124005 - 20 Jun 2024

Viewed by 932

Abstract

Joint source–channel coding (JSCC) based on deep learning has shown significant advancements in image transmission tasks. However, previous channel-adaptive JSCC methods often rely on the signal-to-noise ratio (SNR) of the current channel for encoding, which overlooks the neural network’s self-adaptive capability across varying SNRs. This paper investigates the self-adaptive capability of deep learning-based JSCC models to dynamically changing channels and introduces a novel method named Channel-Blind JSCC (CBJSCC). CBJSCC leverages the intrinsic learning capability of neural networks to self-adapt to dynamic channels and diverse SNRs without relying on external SNR information. This approach is advantageous, as it is not affected by channel estimation errors and can be applied to one-to-many wireless communication scenarios. To enhance the performance of JSCC tasks, the CBJSCC model employs a specially designed encoder–decoder. Experimental results show that CBJSCC outperforms existing channel-adaptive JSCC methods that depend on SNR estimation and feedback, both in additive white Gaussian noise environments and under slow Rayleigh fading channel conditions. Through a comprehensive analysis of the model’s performance, we further validate the robustness and adaptability of this strategy across different application scenarios, with the experimental results providing strong evidence to support this claim. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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18 pages, 529 KiB

Open AccessArticle

Two-Layer Edge Intelligence for Task Offloading and Computing Capacity Allocation with UAV Assistance in Vehicular Networks

by Xiaodan Bi and Lian Zhao

Sensors 2024, 24(6), 1863; https://doi.org/10.3390/s24061863 - 14 Mar 2024

Cited by 2 | Viewed by 1126

Abstract

With the exponential growth of wireless devices and the demand for real-time processing, traditional server architectures face challenges in meeting the ever-increasing computational requirements. This paper proposes a collaborative edge computing framework to offload and process tasks efficiently in such environments. By equipping a moving unmanned aerial vehicle (UAV) as the mobile edge computing (MEC) server, the proposed architecture aims to release the burden on roadside units (RSUs) servers. Specifically, we propose a two-layer edge intelligence scheme to allocate network computing resources. The first layer intelligently offloads and allocates tasks generated by wireless devices in the vehicular system, and the second layer utilizes the partially observable stochastic game (POSG), solved by duelling deep Q-learning, to allocate the computing resources of each processing node (PN) to different tasks. Meanwhile, we propose a weighted position optimization algorithm for the UAV movement in the system to facilitate task offloading and task processing. Simulation results demonstrate the improved performance by applying the proposed scheme. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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15 pages, 922 KiB

Open AccessArticle

A Deep Long-Term Joint Temporal–Spectral Network for Spectrum Prediction

by Lei Wang, Jun Hu, Rundong Jiang and Zengping Chen

Sensors 2024, 24(5), 1498; https://doi.org/10.3390/s24051498 - 26 Feb 2024

Cited by 1 | Viewed by 1220

Abstract

Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and

R^{2}

, respectively, which is better than all the baseline deep learning models. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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24 pages, 3150 KiB

Open AccessArticle

Device-Free Tracking through Self-Attention Mechanism and Unscented Kalman Filter with Commodity Wi-Fi

by Kabo Poloko Nkabiti and Yueyun Chen

Sensors 2023, 23(12), 5527; https://doi.org/10.3390/s23125527 - 13 Jun 2023

Viewed by 1448

Abstract

Recent advancements in target tracking using Wi-Fi signals and channel state information (CSI) have significantly improved the accuracy and efficiency of tracking mobile targets. However, there remains a gap in developing a comprehensive approach that combines CSI, an unscented Kalman filter (UKF), and a sole self-attention mechanism to accurately estimate the position, velocity, and acceleration of targets in real-time. Furthermore, optimizing the computational efficiency of such approaches is necessary for their applicability in resource-constrained environments. To bridge this gap, this research study proposes a novel approach that addresses these challenges. The approach leverages CSI data collected from commodity Wi-Fi devices and incorporates a combination of the UKF and a sole self-attention mechanism. By fusing these elements, the proposed model provides instantaneous and precise estimates of the target’s position while considering factors such as acceleration and network information. The effectiveness of the proposed approach is demonstrated through extensive experiments conducted in a controlled test bed environment. The results exhibit a remarkable tracking accuracy level of 97%, affirming the model’s ability to successfully track mobile targets. The achieved accuracy showcases the potential of the proposed approach for applications in human-computer interactions, surveillance, and security. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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13 pages, 1732 KiB

Open AccessArticle

Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural Network

by Abdallah Elhigazi Abdallah, Mosab Hamdan, Mohammed S. M. Gismalla, Ashraf Osman Ibrahim, Nouf Saleh Aljurayban, Wamda Nagmeldin and Mutaz H. H. Khairi

Sensors 2023, 23(5), 2663; https://doi.org/10.3390/s23052663 - 28 Feb 2023

Cited by 6 | Viewed by 2105

Abstract

Wireless Local Area Networks (WLANs) have become an increasingly popular mode of communication and networking, with a wide range of applications in various fields. However, the increasing popularity of WLANs has also led to an increase in security threats, including denial of service (DoS) attacks. In this study, management-frames-based DoS attacks, in which the attacker floods the network with management frames, are particularly concerning as they can cause widespread disruptions in the network. Attacks known as denial of service (DoS) can target wireless LANs. None of the wireless security mechanisms in use today contemplate defence against them. At the MAC layer, there are multiple vulnerabilities that can be exploited to launch DoS attacks. This paper focuses on designing and developing an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks. The proposed scheme aims to effectively detect fake de-authentication/disassociation frames and improve network performance by avoiding communication interruption caused by such attacks. The proposed NN scheme leverages machine learning techniques to analyse patterns and features in the management frames exchanged between wireless devices. By training the NN, the system can learn to accurately detect potential DoS attacks. This approach offers a more sophisticated and effective solution to the problem of DoS attacks in wireless LANs and has the potential to significantly enhance the security and reliability of these networks. According to the experimental results, the proposed technique exhibits higher effectiveness in detection compared to existing methods, as evidenced by a significantly increased true positive rate and a decreased false positive rate. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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16 pages, 3110 KiB

Open AccessArticle

A Method of Noise Reduction for Radio Communication Signal Based on RaGAN

by Liang Peng, Shengliang Fang, Youchen Fan, Mengtao Wang and Zhao Ma

Sensors 2023, 23(1), 475; https://doi.org/10.3390/s23010475 - 1 Jan 2023

Cited by 4 | Viewed by 5579

Abstract

Radio signals are polluted by noise in the process of channel transmission, which will lead to signal distortion. Noise reduction of radio signals is an effective means to eliminate the impact of noise. Using deep learning (DL) to denoise signals can reduce the dependence on artificial domain knowledge, while traditional signal-processing-based denoising methods often require knowledge of the artificial domain. Aiming at the problem of noise reduction of radio communication signals, a radio communication signal denoising method based on the relativistic average generative adversarial networks (RaGAN) is proposed in this paper. This method combines the bidirectional long short-term memory (Bi-LSTM) model, which is good at processing time-series data with RaGAN, and uses the weighted loss function to construct a noise reduction model suitable for radio communication signals, which realizes the end-to-end denoising of radio signals. The experimental results show that, compared with the existing methods, the proposed algorithm has significantly improved the noise reduction effect. In the case of a low signal-to-noise ratio (SNR), the signal modulation recognition accuracy is improved by about 10% after noise reduction. Full article

(This article belongs to the Special Issue Advances and Applications of Machine Learning for Wireless Communications and Networking)

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