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Deep Learning Approaches for PHY/MAC Wireless Communication and AI Integration
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Deep Learning Approaches for PHY/MAC Wireless Communication and AI Integration

A special issue of AI (ISSN 2673-2688). This special issue belongs to the section "AI Systems: Theory and Applications".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1243

Special Issue Editors

Dr. Eneko Iradier

E-Mail Website
Guest Editor
Department of Computer Languages and Systems, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
Interests: AI-based technologies for the PHY/MAC/RRM layers of wireless communication systems
Special Issues, Collections and Topics in MDPI journals
Dr. Iñigo Bilbao

E-Mail Website
Guest Editor
Department of Applied Mathematics, University of the Basque Country (UPV/EHU), 48013 Bilbao, Spain
Interests: AI-based technologies for the PHY/MAC/RRM layers of wireless communication systems

Special Issue Information

Dear Colleagues,

The move toward 6G wireless systems brings new challenges in spectral and energy efficiency, latency, reliability, and network intelligence. Traditional model-driven methods at the PHY and MAC layers, although grounded in theory, often fall short in dynamic, heterogeneous, large-scale environments. Deep learning offers a new way to address the complex demands of these systems.

This Special Issue highlights recent advances in deep learning for PHY and MAC layers in wireless systems and their integration with AI-driven network architectures. Key neural network types—convolutional, recurrent, graph-based, and transformer—demonstrate promise in channel estimation, signal detection, beamforming, modulation recognition, interference management, resource allocation, and scheduling. Data-driven and hybrid techniques support adaptive, robust solutions under imperfect channel conditions and changing environments.

Beyond single-layer optimization, this Special Issue covers cross-layer and end-to-end learning, integration with edge intelligence, federated learning, and distributed optimization. Emphasis is placed on generalization, interpretability, computational efficiency, real-time use, and coexistence with traditional algorithms.

We invite original contributions that address these topics and demonstrate practical impact. Please submit your latest research, innovative algorithms, systems, or experimental results to help advance the field of AI-enabled PHY/MAC wireless communication systems. Join us in shaping the future of wireless communications through this Special Issue.

Dr. Eneko Iradier
Dr. Iñigo Bilbao
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. AI is an international peer-reviewed open access monthly 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 1800 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

  • deep learning for wireless communications
  • PHY layer signal processing
  • MAC layer optimization
  • AI-enabled wireless networks
  • cross-layer learning
  • channel estimation and detection
  • resource allocation and scheduling
  • 6G systems
  • edge intelligence and federated learning

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Published Papers (1 paper)

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15 pages, 629 KB  
Article
Tiny Neural Receiver: Enabling On-Device Learning for Scalable and Adaptive 6G Devices
by Iñigo Bilbao, Eneko Iradier, Jon Montalban, Marta Fernández, Iñaki Eizmendi and Pablo Angueira
AI 2026, 7(4), 144; https://doi.org/10.3390/ai7040144 - 17 Apr 2026
Viewed by 964
Abstract
The evolution toward 6G communications requires integrating Tiny Machine Learning (TinyML) principles to enable intelligent, energy-efficient, and adaptable signal processing at the network edge. However, current receiver architectures face a fundamental trade-off: classical model-driven designs, while naturally efficient due to their basis in [...] Read more.
The evolution toward 6G communications requires integrating Tiny Machine Learning (TinyML) principles to enable intelligent, energy-efficient, and adaptable signal processing at the network edge. However, current receiver architectures face a fundamental trade-off: classical model-driven designs, while naturally efficient due to their basis in communication theory, lack the flexibility to adapt to varying channel conditions. Meanwhile, fully data-driven deep-learning-based approaches break the stringent resource constraints of TinyML. This paper introduces the tiny neural receiver (TNR), a pioneering architecture that bridges these paradigms by integrating model-based signal processing with lightweight neural optimization to overcome this challenge. The TNR’s primary contribution is its unique hybrid design, which combines the efficiency and interpretability of traditional theory-based receivers with the ability to adapt to different contexts using trainable neural components. This integration occurs within resource budgets that align with TinyML specifications. Experimental results show that the TNR achieves a 5 dB SNR reduction at a target block error rate of 10−4. The reported 5 dB SNR gain is a direct result of our resource-aware design framework, which selectively applies lightweight neural optimization to only the most impactful receiver blocks (channel estimation and decoding) to maximize gain without exceeding TinyML complexity limits. This achievement is further supported by an end-to-end training protocol that uses 15,000 iterations of over-the-air data to fine-tune these parameters for the specific static 3.5 GHz propagation channel and OFDM configuration evaluated. Furthermore, the TNR’s modular design enables flexible deployment across a range of 6G scenarios, from mobile broadband to mission-critical IoT. This establishes the TNR as a promising framework for AI-native 6G receivers. Full article
(This article belongs to the Special Issue Deep Learning Approaches for PHY/MAC Wireless Communication and AI Integration)
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