Applications of Artificial Intelligence and Machine Learning in Communications and Networks

A special issue of Network (ISSN 2673-8732).

Deadline for manuscript submissions: 31 May 2025 | Viewed by 488

Special Issue Editors


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Guest Editor
Department of Information and Communication Technology, University of Agder, Grimstad, Norway
Interests: AI/ML in communication/networking; 5G new radio; Internet of Things; machine-type communications; hybrid communication systems; joint sensing and communication; UAV-assisted communications; small world networks; social networks

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Guest Editor
Faculty of Engineering, Free University of Bozen-Bolzano, 39100 Bozen-Bolzano, Italy
Interests: biometrics (physical/behavioral); authentication and access control using human behaviors; machine learning; data mining; generative adversarial networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the transformative applications of AI and ML in communication systems and networking. These technologies are driving a paradigm shift in wireless networks, effectively challenging traditional heuristic and algorithm-based solutions, including routing protocols and traffic engineering. The current literature increasingly integrates AI-driven methods into signal processing and channel coding, enhancing performance beyond the limits of conventional mathematical models.

A significant focus is placed on the role of AI and ML in the evolution of 5G and future 6G networks. It is widely recognized that these technologies are essential for achieving ultra-low latency, extensive device connectivity, and high reliability, thus unlocking the full potential of next-generation networks.

Additionally, AI and ML applications within communication networks are closely linked to advancements in cybersecurity. This includes critical areas such as anomaly detection, fraud identification through pattern recognition, and protective measures against distributed denial-of-service (DDoS) attacks. Overall, this Special Issue underscores the growing importance of AI and ML in shaping the future landscape of communications and networks.

The scope of this Special Issue is broad, given below:

  • Network Optimization and Management
  • Resource Allocation
  • Congestion Control
  • Channel estimation and modeling
  • Beamforming
  • Cognitive Radios
  • Security of AI-enable
  • Edge Computing
  • Cloud Computing
  • UAV-Assisted Wireless Networks
  • Social Networks
  • Small-world Networks
  • Network Slicing
  • Optimization in Communication
  • Non-Orthogonal Multiple Access

Dr. Sreenivasa Reddy Yeduri
Dr. Attaullah Buriro
Guest Editors

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. Network is an international peer-reviewed open access quarterly 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 1000 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

  • artificial intelligence (AI) in networking
  • machine learning (ML) for communications
  • AI-driven network management
  • deep learning in communication systems
  • AI for 5G and 6G networks
  • network security with AI/ML
  • AI for resource allocation
  • edge AI and edge computing

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Published Papers (1 paper)

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Research

16 pages, 2521 KiB  
Article
Age of Information Minimization in Vehicular Edge Computing Networks: A Mask-Assisted Hybrid PPO-Based Method
by Xiaoli Qin, Zhifei Zhang, Chanyuan Meng, Rui Dong, Ke Xiong and Pingyi Fan
Network 2025, 5(2), 12; https://doi.org/10.3390/network5020012 - 14 Apr 2025
Viewed by 157
Abstract
With the widespread deployment of various emerging intelligent applications, information timeliness is crucial for intelligent decision-making in vehicular networks, where vehicular edge computing (VEC) has become an important paradigm to enhance computing capabilities by offloading tasks to edge nodes. To promote the information [...] Read more.
With the widespread deployment of various emerging intelligent applications, information timeliness is crucial for intelligent decision-making in vehicular networks, where vehicular edge computing (VEC) has become an important paradigm to enhance computing capabilities by offloading tasks to edge nodes. To promote the information timeliness in VEC, an optimization problem is formulated to minimize the age of information (AoI) by jointly optimizing task offloading and subcarrier allocation. Due to the time-varying channel and the coupling of the continuous and discrete optimization variables, the problem exhibits non-convexity, which is difficult to solve using traditional mathematical optimization methods. To efficiently tackle this challenge, we employ a hybrid proximal policy optimization (HPPO)-based deep reinforcement learning (DRL) method by designing the mixed action space involving both continuous and discrete variables. Moreover, an action masking mechanism is designed to filter out invalid actions in the action space caused by limitations in the effective communication distance between vehicles. As a result, a mask-assisted HPPO (MHPPO) method is proposed by integrating the action masking mechanism into the HPPO. Simulation results show that the proposed MHPPO method achieves an approximately 28.9% reduction in AoI compared with the HPPO method and about a 23% reduction compared with the mask-assisted deep deterministic policy gradient (MDDPG). Full article
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