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Electronics
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AI-Native Ubiquitous 6G: Key Technologies, Architectures, and Applications
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AI-Native Ubiquitous 6G: Key Technologies, Architectures, and Applications

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 811

Special Issue Editors

Dr. Yuanbin Chen

E-Mail Website
Guest Editor
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
Interests: holographic MIMO; XL-MIMO; RIS; signal processing
Special Issues, Collections and Topics in MDPI journals
Dr. Jiakang Zheng

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
Interests: cell-free massive MIMO; mobile communications; performance analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Zile Liu

E-Mail Website
Guest Editor
Department of Electronic Engineering, Tsinghua University, Beijing, China
Interests: terahertz wireless communications; low-altitude wireless networks (LAWN) and information theory on integrated sensing and communications (ISAC)

Special Issue Information

Dear Colleagues,

Ubiquitous future 6G wireless networks are expected to move beyond the conventional pursuit of higher peak rates and denser coverage. Instead, 6G is increasingly envisioned as a unified cyber–physical–intelligent fabric that tightly couples communication, sensing, computing, and control to support a broad spectrum of services—from immersive human-centric experiences (XR and holographic telepresence) to mission-critical industrial automation, cooperative robotics, and massive machine-type connectivity. Such an evolution introduces unprecedented system requirements, including extreme reliability and low latency, fine-grained spatial and temporal awareness, scalable energy efficiency, and end-to-end trustworthiness under heterogeneous and dynamic environments. Meanwhile, emerging technologies—such as extremely large/aperture arrays, holographic MIMO and reconfigurable intelligent surfaces, integrated sensing and communications, edge–cloud continuum computing, and AI-native network orchestration—are fundamentally reshaping how wireless networks are designed, optimized, and operated.

In this context, this Special Issue aims to bring together key enabling theories, algorithms, frameworks, architectures, and prototypes that can realize truly ubiquitous 6G connectivity and intelligence. By emphasizing AI-native design, data-driven automation, and cross-layer co-optimization, the collection seeks to foster convergent research that bridges physical-layer innovations and network-level intelligence, enabling practical pathways toward scalable, deployable, and resilient 6G systems. We particularly welcome original contributions across (but not limited to) the following topics.

The collection spans theory, algorithms, frameworks, architectures, and protype across the following:

  • AI-enabled 6G networks/services/applications/scenarios;
  • Large language models (LLMs) and foundation models for 6G operations/orchestrations;
  • Digital twins for real-time 6G network cognition/prediction/optimization/closed-loop control;
  • 6G new antenna and propagation paradigms;
  • 6G advanced signal processing techniques and algorithms;
  • 6G resource allocation and scheduling for heterogeneous services;
  • 6G edge-cloud computing and distributed intelligence;
  • 6G wireless network security/privacy and trust;
  • Representative 6G application scenarios (e.g., XR, industrial automation, vehicular/robotic networks, IoT/IoE, integrating sensing and communications).

Dr. Yuanbin Chen
Dr. Jiakang Zheng
Dr. Zile Liu
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

  • 6G
  • AI
  • digital twin

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Published Papers (2 papers)

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35 pages, 83521 KB  
Article
AI-Native Multi-Scale Attention Fusion for Ubiquitous Aerial Sensing: Small Object Detection in UAV Imagery
by Ke Ma, Zhongjie Zhang, Jiarui Zhang and Jian Huang
Electronics 2026, 15(5), 1100; https://doi.org/10.3390/electronics15051100 - 6 Mar 2026
Viewed by 302
Abstract
Ubiquitous aerial sensing with unmanned aerial vehicles (UAVs) is becoming an essential component of AI-native perception systems, motivated by the trend toward edge deployment and potential integration with future sixth-generation (6G)-connected aerial networks. In this work, we focus on improving the perception-side accuracy [...] Read more.
Ubiquitous aerial sensing with unmanned aerial vehicles (UAVs) is becoming an essential component of AI-native perception systems, motivated by the trend toward edge deployment and potential integration with future sixth-generation (6G)-connected aerial networks. In this work, we focus on improving the perception-side accuracy and computational efficiency of small-object detection in UAV imagery. However, small object detection in high-altitude UAV imagery remains highly challenging due to the extremely low pixel occupancy of targets and the severe multi-scale interference introduced by complex backgrounds. To address these limitations, we propose a Multi-scale Attention Fusion Network (MAF-Net), an AI-native paradigm for real-time small object detection in UAV imagery. The proposed approach enhances small-target representation and robustness through three key designs. First, a density-adaptive anchor optimization strategy is developed by combining K-means++ clustering with an IoU-based distance metric, enabling anchors to better match scale variation under diverse object densities. Second, a multi-scale feature reinforcement module is introduced to strengthen fine-grained detail preservation by integrating shallow feature maps via skip connections and hierarchical aggregation. Third, a dual-path attention mechanism is employed to jointly model channel importance and spatial localization, improving discriminative feature calibration in cluttered aerial scenes. Extensive experiments on three public benchmarks (AI-TOD, DOTA, and RSOD) demonstrate that MAF-Net consistently outperforms the baseline detector, achieving mAP@0.5 gains of 14.1%, 11.28%, and 22.09%, respectively. These results confirm that MAF-Net provides an effective and deployment-friendly solution for robust small object detection, supporting real-time UAV-based inspection and AI-native ubiquitous aerial sensing applications. Full article
(This article belongs to the Special Issue AI-Native Ubiquitous 6G: Key Technologies, Architectures, and Applications)
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24 pages, 1133 KB  
Article
Distributed Privacy-Preserving Fusion for Multi-UAV Target Localization via Free-Noise Masking
by Ke Ma, Guowei Pan and Jian Huang
Electronics 2026, 15(5), 1016; https://doi.org/10.3390/electronics15051016 - 28 Feb 2026
Viewed by 236
Abstract
Multi-UAV target localization relies on cooperative fusion of local, perception-derived geometric measurements over an edge network. While distributed fusion improves scalability and robustness compared with a centralized architecture, the iterative message exchanges may leak sensitive information to external eavesdroppers or honest-but-curious peers. This [...] Read more.
Multi-UAV target localization relies on cooperative fusion of local, perception-derived geometric measurements over an edge network. While distributed fusion improves scalability and robustness compared with a centralized architecture, the iterative message exchanges may leak sensitive information to external eavesdroppers or honest-but-curious peers. This paper proposes a privacy-preserving distributed fusion method for multi-UAV localization via free-noise masking. The key idea is a double-injection mechanism. Specifically, each UAV masks its transmitted iterate with a locally generated bounded noise vector, while injecting the same noise into its local update so that the perturbations cancel exactly in the network-average dynamics under doubly stochastic mixing. As a result, the proposed PPDO-FN scheme preserves the practical convergence and weighted least squares localization accuracy of non-private distributed gradient descent, without requiring heavy cryptography or a trusted server. We further introduce reconstruction-based privacy metrics under transcript attacks and quantify the privacy–accuracy tradeoff. Simulation results demonstrate (i) near-identical accuracy and consensus behavior to the non-private baseline, (ii) monotonic privacy improvement with increasing masking strength, and (iii) the necessity of double-injection canceling compared with a naive single-injection baseline. Finally, we provide an end-to-end case study to connect the image-level detection to the geometric localization and then to privacy-preserving distributed fusion, illustrating engineering viability for our proposed approach. Full article
(This article belongs to the Special Issue AI-Native Ubiquitous 6G: Key Technologies, Architectures, and Applications)
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