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Electronics
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Optimization and Machine Learning for Wireless Communications
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Optimization and Machine Learning for Wireless Communications

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 5308

Special Issue Editors

Dr. Mohammed H. Alsharif

E-Mail Website
Guest Editor
College of Electronics and Information Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: wireless communications; network information theory; internet of things; green communication; sustainable cellular networks
Special Issues, Collections and Topics in MDPI journals
Dr. Kannadasan Raju

E-Mail Website
Guest Editor
Department of Electrical and Electronics Engineering, Sri Venkateswara College of Engineering, Sripeumbudur, Chennai, India
Interests: renewable energy resources; micro-grid; electric vehicles; smart waste management; Internet of Things (IoT); metal oxide surge arrester; distributed generations; repowering of the wind farm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Contemporarily, optimization and machine learning techniques in engineering and science is the most rapidly developing research area seeking to improve communication systems and networks. However, the field faces complex challenges due to the high degree of complexity; therefore, it is essential to introduce sophisticated approaches, tools, and techniques that help stakeholders and decision-makers to make decisions in consideration of the broad range of uncertainty with data sets. Sophisticated soft computing techniques have the potential to solve such complex problems in various applications, irrespective of the domain. The main objective of this Special Issue is to consolidate the most advanced optimization and machine learning approaches to solve the cumbersome problems in wireless communications. Both original research and review articles are welcome. Potential topics include, but are not limited to, the following:

  • Optimization in wireless communications;
  • Resource optimization in 6G/5G/LTE/WiFi applications;
  • Model-based machine learning for communications;
  • Convex optimization for signal processing and communications;
  • Machine learning for wireless networks;
  • Deep neural networks for joint source-channel coding;
  • Constrained unsupervised learning for wireless network optimization;
  • Capacity estimation using machine learning;
  • Low-complexity, approximate solutions for difficult non-convex problems in wireless communications.

We hope that this Special Issue will achieve a precise, concrete, and concise conclusion that contributes significantly to opening new horizons for future research directions in the field of wireless communications.

Dr. Mohammed H. Alsharif 
Dr. Kannadasan Raju
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. 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.

Keywords

  • wireless communications
  • Internet of Things
  • communications systems
  • sensor networks
  • smart cities
  • optimization
  • machine learning
  • deep learning
  • artificial intelligence

Published Papers (4 papers)

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Research

15 pages, 2108 KiB  
Article
Performance Evaluation of Single-Carrier and Orthogonal Frequency Divison Multiplexing-Based Autoencoders in Comparison with Low-Density Parity-Check Encoder
by Nguyen Tan HP, Bang Le Thanh, Thanh-Nha To, Hoang-Lai Pham, Viet-Hai Dinh, Tien-Thanh Nguyen and Bang Khuc
Electronics 2023, 12(18), 3945; https://doi.org/10.3390/electronics12183945 - 19 Sep 2023
Viewed by 726
Abstract
Recently, the growing demands for ultra-high speed applications require more advanced and optimal data transmission techniques. Wireless autoencoders have gained significant attention since they provide global optimization of the transceiver structure. This article explores the application of autoencoders to enhance the performance of [...] Read more.
Recently, the growing demands for ultra-high speed applications require more advanced and optimal data transmission techniques. Wireless autoencoders have gained significant attention since they provide global optimization of the transceiver structure. This article explores the application of autoencoders to enhance the performance of wireless communication systems. It provides the performance evaluation of the systems using single-carrier and OFDM-based autoencoders under the conditions of AWGN and fading channels. Then, in terms of the BLER metric, the wireless systems with autoencoders are compared with conventional systems using LDPC coding and quadrature amplitude modulation for various configurations. Simulation results indicate that for high-modulation orders (QAM-64 or QAM-256), communication systems employing autoencoders provide superior performance compared to systems using LDPC channel encoding in regions with a low signal-to-noise (SNR) ratio. Specifically, a gain of 1–2 dB in signal power is obtained for single-carrier autoencoders and 0.3–2 dB is obtained for OFDM-based autoencoders. Therefore, wireless communication systems utilizing autoencoders can be considered as a promising candidate for future wireless communication systems. Full article
(This article belongs to the Special Issue Optimization and Machine Learning for Wireless Communications)
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17 pages, 1578 KiB  
Article
CascadMLIDS: A Cascaded Machine Learning Framework for Intrusion Detection System in VANET
by Argha Chandra Dhar, Arna Roy, M. A. H. Akhand and Md Abdus Samad Kamal
Electronics 2023, 12(18), 3779; https://doi.org/10.3390/electronics12183779 - 07 Sep 2023
Viewed by 976
Abstract
Vehicular ad hoc networks (VANETs) incorporating vehicles as an active and fast topology are gaining popularity as wireless communication means in intelligent transportation systems (ITSs). The cybersecurity issue in VANETs has drawn attention due to the potential security threats these networks face. An [...] Read more.
Vehicular ad hoc networks (VANETs) incorporating vehicles as an active and fast topology are gaining popularity as wireless communication means in intelligent transportation systems (ITSs). The cybersecurity issue in VANETs has drawn attention due to the potential security threats these networks face. An effective cybersecurity measure is essential as security threats impact the overall system, from business disruptions to data corruption, theft, exposure, and unauthorized network access. Intrusion detection systems (IDSs) are popular cybersecurity measures that detect intrusive behavior in a network. Recently, the machine learning (ML)-based IDS has emerged as a new research direction in VANET security. ML-based IDS studies have focused on improving accuracy as a typical classification task without focusing on malicious data. This study proposes a novel IDS for VANETs that offers more attention to classifying attack cases correctly with minimal features required by applying principal component analysis. The proposed Cascaded ML framework recognizes the difference between the attack and normal cases in the first step and classifies the attack data in the second step. The framework emphasizes that an attack should not be classified into the normal class. Finally, the proposed framework is implemented with an artificial neural network, the most popular ML model, and evaluated with the Car Hacking dataset. In addition, the study also investigates the efficiency of typical classification tasks and compares them with results of the proposed framework. Experimental results on the Car Hacking dataset have revealed the proposed method to be an effective IDS and that it outperformed the existing state-of-the-art ML models. Full article
(This article belongs to the Special Issue Optimization and Machine Learning for Wireless Communications)
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24 pages, 6575 KiB  
Article
Handover Triggering Prediction with the Two-Step XGBOOST Ensemble Algorithm for Conditional Handover in Non-Terrestrial Networks
by Eunsu Kim and Inwhee Joe
Electronics 2023, 12(16), 3435; https://doi.org/10.3390/electronics12163435 - 14 Aug 2023
Viewed by 880
Abstract
A Non-Terrestrial Network (NTN) is a network system that enables service for areas where terrestrial networks cannot cover. An NTN provides communication services using flying objects such as UAVs, HAPs, and satellites. In the case of satellites, they move in Earth’s orbit at [...] Read more.
A Non-Terrestrial Network (NTN) is a network system that enables service for areas where terrestrial networks cannot cover. An NTN provides communication services using flying objects such as UAVs, HAPs, and satellites. In the case of satellites, they move in Earth’s orbit at a constant speed. Ground services from continuously moving satellites cause frequent handovers. In addition, frequent handovers may come as a load between User Equipment (UE) and the communication system, which leads to degradation of service quality. Unlike Terrestrial Networks (TN), communication services are provided to UEs at altitudes ranging from 20 km to 35,584 km, rather than from base stations close to the ground. Service at high altitudes is unreliable due to the measurement values that were previously used as quality indicators to operate terrestrial networks. Moreover, service at high altitudes demands long-distance communication, and propagation delay occurs from the long-distance communication. In the 3GPP Rel. 17 document, it is suggested that the above problems should be solved. This paper tries to solve the problem by proposing the two-step XGBOOST, a CART-based Gradient Boosting Model. Handover in TN uses measurement-based conditional handover (CHO), but the measured values in the NTN environment are not valid. Using this, the distance between the UE and the center of the cell and the elevation angle are used to construct a model that predicts the HO triggering time point. In order to overcome the propagation delay caused by communication at a high altitude, a model that predicts the distance and elevation angle between the UE and the center of the cell considering the propagation delay is proposed. The model is composed of two-step XGBOOST. The one-step model is a model in which the UE predicts the distance and elevation angle between cell centers after propagation delay at the time when satellite position information is transmitted to the UE. The two-step model predicts handover triggering occurrence based on the data predicted by the one-step result. As a result of the experiment, the model considering the propagation delay showed about 8% better performance on average than the model not considering the propagation delay, and the XGBOOST model achieved an average F1-score of 0.9891 in the propagation delay experiments. Full article
(This article belongs to the Special Issue Optimization and Machine Learning for Wireless Communications)
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18 pages, 1667 KiB  
Article
Smart Load-Based Resource Optimization Model to Enhance the Performance of Device-to-Device Communication in 5G-WPAN
by Jaganathan Logeshwaran, Thangavel Kiruthiga, Raju Kannadasan, Loganathan Vijayaraja, Ali Alqahtani, Nayef Alqahtani and Abdulaziz A. Alsulami
Electronics 2023, 12(8), 1821; https://doi.org/10.3390/electronics12081821 - 12 Apr 2023
Cited by 23 | Viewed by 1608
Abstract
In wireless personal area networks (WPANs), devices can communicate with each other without relying on a central router or access point. They can improve performance and efficiency by allowing devices to share resources directly; however, managing resource allocation and optimizing communication between devices [...] Read more.
In wireless personal area networks (WPANs), devices can communicate with each other without relying on a central router or access point. They can improve performance and efficiency by allowing devices to share resources directly; however, managing resource allocation and optimizing communication between devices can be challenging. This paper proposes an intelligent load-based resource optimization model to enhance the performance of device-to-device communication in 5G-WPAN. Intelligent load-based resource optimization in device-to-device communication is a strategy used to maximize the efficiency and effectiveness of resource usage in device-to-device (D2D) communications. This optimization strategy is based on optimizing the network’s resource load by managing resource utilization and ensuring that the network is not overloaded. It is achieved by monitoring the current load on the network and then adjusting the usage of resources, such as bandwidth and power, to optimize the overall performance. This type of optimization is essential in D2D communication since it can help reduce costs and improve the system’s performance. The proposed model has achieved 86.00% network efficiency, 93.74% throughput, 91.94% reduced latency, and 92.85% scalability. Full article
(This article belongs to the Special Issue Optimization and Machine Learning for Wireless Communications)
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