Advances in Intelligent Wireless Communications: AI, Optimization, and Beyond

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 105

Special Issue Editors

Department of Information and Telecommunication Engineering, College of Information Technology, Incheon National University, Incheon 22012, Republic of Korea
Interests: wireless communications; networking; optimization; intelligent decision-making
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Guest Editor
Department of IT Convergence, ICT Polytech Institute of Korea, Gwangju 12777, Republic of Korea
Interests: wireless communication; mobile network; machine learning; resource allocation; traffic control; convex optimization

Special Issue Information

Dear Colleagues,

The rapid advancement of wireless communication technologies, particularly in the context of 5G, 6G, and massive IoT deployments, has introduced unprecedented levels of complexity, heterogeneity, and performance demands in modern communication systems. Traditional design paradigms, often based on fixed models and static optimization, are increasingly challenged by dynamic environments, diverse quality-of-service requirements, and resource constraints. In response, the integration of intelligent methodologies—encompassing artificial intelligence (AI), machine learning (ML), and advanced optimization—has gained significant attention as a means to enable adaptive, efficient, and scalable wireless communication networks.

This Special Issue, entitled “Advances in Intelligent Wireless Communications: AI, Optimization, and Beyond,” seeks to explore recent developments and emerging research at the intersection of wireless systems and intelligent decision-making techniques. The aim is to gather high-quality contributions that address both theoretical foundations and practical implementations of intelligent approaches across all layers of wireless networks. This includes novel frameworks, algorithms, and architectures that improve spectrum utilization, energy efficiency, network reliability, and service adaptability.

Topics of interest include, but are not limited to, the following:

  • Intelligent signal processing.
  • Adaptive and cognitive protocol design.
  • Resource optimization and control.
  • Learning-based and heuristic approaches.
  • Data-driven network management and orchestration.
  • Distributed and edge intelligence.
  • Applications in IoT, UAVs, vehicular networks, and next-generation systems.

We welcome original research articles and comprehensive reviews that advance the state of the art in intelligent wireless communications.

We look forward to receiving your contributions.

Dr. Do-Yup Kim
Dr. Sung-Yeon Kim
Guest Editors

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Keywords

  • intelligent wireless communication networks
  • machine learning and AI technologies
  • resource allocation and optimization
  • federated and distributed learning
  • cognitive and self-organizing systems
  • energy-efficient network design
  • data-driven and adaptive control
  • next-generation wireless applications

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Published Papers (1 paper)

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Research

12 pages, 542 KB  
Article
Expensive Highly Constrained Antenna Design Using Surrogate-Assisted Evolutionary Optimization
by Caie Hu, Sanyou Zeng and Changhe Li
Electronics 2025, 14(18), 3613; https://doi.org/10.3390/electronics14183613 - 11 Sep 2025
Abstract
Antenna structure design constitutes a computationally expensive optimization problem due to the requirement for full-wave electromagnetic (EM) simulations. Surrogate-assisted evolutionary algorithms offer a promising approach for addressing such challenges. However, several challenges remain in solving expensive, highly constrained antenna design problems. This paper [...] Read more.
Antenna structure design constitutes a computationally expensive optimization problem due to the requirement for full-wave electromagnetic (EM) simulations. Surrogate-assisted evolutionary algorithms offer a promising approach for addressing such challenges. However, several challenges remain in solving expensive, highly constrained antenna design problems. This paper introduces a surrogate-assisted dynamic constrained multi-objective evolutionary algorithm framework to tackle expensive and highly constrained antenna design optimization tasks. A multi-layer perceptron (MLP) is employed as the surrogate model to approximate EM evaluations and alleviate the computational burden, while a dynamic scale-constrained boundary strategy is implemented to handle highly constraints. The effectiveness of the proposed method is validated on a set of constrained benchmark problems and two antenna design cases. Full article
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