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Advances in AI for 6G Signal Processing

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

Deadline for manuscript submissions: 15 August 2026 | Viewed by 1423

Special Issue Editors


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Guest Editor
Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
Interests: mobile communications; forward error correction coding; reconfigurable (software radio) architectures; cross-layer architectures; V2V applications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

E-Mail Website
Guest Editor
1. Department of Digital Systems, School of Technology, University of Thessaly, Geopolis, 41500 Larissa, Greece
2. Department of Electrical & Computer Engineering, University of Thessaly, 38334 Volos, Greece
Interests: security and privacy in wireless communications; vehicular ad-hoc networks (VANETs); estimation techniques for physical layers; error detection and correction techniques in physical layers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditional mobile communications are well designed and have been extensively deployed by mobile operators. However, future 6G systems consider more complicated communication and network scenarios such as automated vehicles, factory automation, telemedicine, and so on; thus, the traditional approach may not be suitable for supporting multiple 6G requirements. Furthermore, Artificial Intelligence (AI) now seems to dominate many aspects of current technology. In 6G wireless systems, the data-driven approach of AI algorithms will be equipped with heuristic parameter settings and thresholds. A promising concept regarding the improvement of 6G system design and optimization is the adoption of AI algorithms. This is because AI algorithms already play a key role in research fields such as image recognition (deep learning).

The optimization parameters associated with wireless communication systems are complexity, cost, energy, latency, throughput, and so on. In this regard, AI and Machine Learning (ML) algorithms will be employed to solve multi-parameter optimization problems, enhance the performance of  6G and develop new services. It is well known that AI and ML algorithms are utilized for classification, clustering, regression, dimension reduction and decision making. It appears that many 6G signal processing elements have increased levels of similarity to these uses. For example, resource allocation and scheduling represent a classification and clustering problem. Moreover, channel estimation represents a regression problem, Viterbi decoding is based on dynamic programming, and network traffic management corresponds to a sequential decision-making problem. In this Special Issue, we discuss the domains of signal processing in which chain AI and ML algorithms can be implemented in 6G systems. The key technical aspects to be investigated regarding the application of AI algorithms to wireless communications are as follows:

  • Collection and data processing using AI algorithms;
  • Matching of signal processing elements for wireless communications with processes, functions and applications of AI algorithms;
  • The key parameters for the measurement of the performance of wireless communications are throughput, energy efficiency, latency and others. AI algorithms have to improve these parameters;
  • AI algorithms have limitations such as an extensive training data set and high computational power, which must be considered;
  • AI algorithms and Shannon-based current wireless communications systems have to find a common theoretical background.

This Special Issue aims to present an overview of recent advances in AI processes and algorithms with regard to the following research areas (topics) for future 6G mobile communications systems:

  • Physical-layer signal processing with the aid of AI (modulation, error correction coding, power level, MIMO techniques, channel estimation in multicarrier systems, etc.)
  • Data-link-layer signal processing with the aid of AI (resource allocation and scheduling, handover, etc.)
  • Network-layer signal processing with the aid of AI (cell planning, network traffic, etc.)
  • AI-based cross-layer optimization techniques (Cybersecurity, real-time data transmission for vehicle to vehicle communication, Internet of Things, wireless sensor networks,)

Dr. Costas Chaikalis
Dr. Apostolis Xenakis
Dr. Dimitrios Kosmanos
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 250 words) can be sent to the Editorial Office for assessment.

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

  • artificial intelligence
  • 6G
  • signal processing
  • wireless communications

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Published Papers (1 paper)

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Research

16 pages, 938 KB  
Article
AI-Enabled Autoencoder-Based Physical Layer Design for 6G Communication Systems
by Andreani Christopoulou, Dimitrios Kosmanos, Apostolos Xenakis and Costas Chaikalis
Electronics 2026, 15(3), 538; https://doi.org/10.3390/electronics15030538 - 26 Jan 2026
Viewed by 451
Abstract
Next-generation wireless communication 6G systems are expected to operate under diverse channel conditions and structures, requiring flexible and data-driven communication schemes. As traditional techniques face limitations in complex and dynamic environments, trained communication architectures have emerged as promising alternatives. In this paper, we [...] Read more.
Next-generation wireless communication 6G systems are expected to operate under diverse channel conditions and structures, requiring flexible and data-driven communication schemes. As traditional techniques face limitations in complex and dynamic environments, trained communication architectures have emerged as promising alternatives. In this paper, we present a thorough study on deep learning trained physical layer components, focusing on autoencoder-based transceivers and neural network modules that enhance the receiver’s intelligence. We further investigate two essential deep learning capabilities for modern receivers—modulation classification using neural architectures and generative data synthesis for channel estimation training. Moreover, the proposed models and simulation framework provide insight into how deep learning can be systematically integrated into the physical layer to improve adaptability, robustness, and efficiency. Full article
(This article belongs to the Special Issue Advances in AI for 6G Signal Processing)
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