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Advanced B5G/6G Communications

A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Communications".

Deadline for manuscript submissions: 28 December 2026 | Viewed by 1389

Editors


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Guest Editor
IEEE Member, Nextome S.r.l., Parma, Italy
Interests: wireless sensor network; information-centric networking; IoT and industrial IoT applications; LPWAN communications; IoT-aided robotic systems; unmanned vehicles; Internet of Drones

E-Mail Website
Guest Editor
Telecommunications at the Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
Interests: adaptive wireless communications; internet of things; cognitive radio access; medium access control; random access; multi-antenna systems; energy-efficiency; resource management in vehicular communica
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Telecommunications at the Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy
Interests: machine learning and data analytics for network optimization; artificial intelligence for networking; 6G systems; integrated terrestrial and non-terrestrial networks

E-Mail Website
Guest Editor Assistant
Joint Research Centre, European Commission, Ispra, Italy
Interests: counter-UAS; non-terrestrial networks; secure 6G networks; Internet of Drones; modeling and simulation

Special Issue Information

Dear Colleagues,

The transition toward Beyond‑5G and 6G networks is unfolding in parallel with several global shifts that are reshaping expectations for wireless communication. The growing reliance on autonomous systems, the rapid spread of distributed sensing technologies and the need for dependable connectivity across heterogeneous environments are pushing current infrastructures to their limits. These pressures are particularly evident in applications that demand extremely low latency, high reliability and the ability to integrate communication and sensing within a single operational framework. At the same time, the increasing use of unmanned aerial vehicles for monitoring, logistics and emergency response, together with the expansion of Low Earth Orbit satellite constellations, is broadening the spatial reach of communication systems and introducing new forms of network heterogeneity.

These developments create opportunities for more resilient and adaptive architectures, but they also raise questions that require coordinated research efforts. The coexistence of terrestrial, aerial and orbital segments calls for new approaches to channel characterization, resource allocation and network management. Emerging technologies such as reconfigurable intelligent surfaces, Terahertz links and AI‑driven control mechanisms offer promising tools to address these challenges, yet their integration into large‑scale systems remains an open problem. The scientific community is therefore confronted, on the one hand, with a landscape in which sensing, communication and computation must be treated as interdependent components rather than isolated functions; on the other hand, the integration of these highly mobile actors introduces security and safety risks throughout the communication infrastructure, calling for multi-modal sensor networks able to detect, track and identify malicious actors, as well as introduce spatial awareness to ensure a safe blending between cyber and physical worlds.

At the same time, emerging paradigms such as semantic communications are gaining attention for their potential to enhance efficiency and intelligence in future networks by prioritizing the transmission of meaningful information rather than raw data. In parallel, advances in quantum-enabled technologies—including quantum-secure communication and quantum-aided networking concepts—are beginning to influence the design of trustworthy communication infrastructures for next-generation wireless systems.

This Special Issue aims to present and disseminate recent advances in sensing‑integrated communication systems, aerial and satellite‑supported networking and intelligent architectures for secure and safe B5G and 6G technologies. Contributions are invited to explore theoretical models, algorithmic solutions, experimental studies and system‑level analyses. The scope includes research on extremely low‑latency communication, joint communication‑sensing strategies, UAV‑assisted networks, LEO satellite systems and the coordination of multi‑layer infrastructures. Works addressing intelligent surfaces, distributed learning and advanced signal processing for next‑generation networks, as well as aerial safety and security mechanisms, are also encouraged.

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

  • Integrated Sensing And Communication (ISAC) for B5G/6G
  • Extremely low‑latency communication techniques and architectures
  • UAV‑assisted communication and sensing systems
  • Multi-modal sensor networks and approaches to detect, track and identify malicious mobile actors
  • Spatial awareness of aerial and space network nodes
  • Satellite communications for B5G/6G, including LEO constellations
  • 3D networking integrating terrestrial, aerial and orbital segments
  • Reconfigurable Intelligent Surfaces (RIS) for communication and sensing
  • TeraHertz (THz) communication and high‑resolution sensing
  • AI‑native network architectures, distributed intelligence and digital twins
  • Semantic communications and meaning-aware networking for B5G/6G system
  • Quantum-secure communication and quantum-aided networking techniques for future wireless infrastructures
  • Resource management, beamforming and mobility support in multi‑layer networks
  • Energy‑efficient communication and sensing strategies
  • Security, privacy and trustworthiness in sensing‑enhanced 6G systems

Dr. Pietro Boccadoro
Dr. Nicola Cordeschi
Dr. Arcangela Rago
Guest Editors

Dr. Grieco Giovanni
Guest Editor Assistant

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors 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 2600 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

  • integrated sensing and communication
  • UAV‑enabled sensing networks
  • Low Earth Orbit satellite systems
  • extremely low‑latency wireless links
  • reconfigurable intelligent surfaces
  • terahertz communication and sensing
  • AI‑driven multi‑layer networking
  • multi-modal detection
  • tracking and identification
  • spatial awareness
  • semantic communications and meaning-aware networking
  • quantum-secure communications and quantum-aided networking

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Published Papers (2 papers)

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Research

15 pages, 3901 KB  
Article
Digital Twin-Assisted Beamforming for Millimeter Wave Massive MIMO
by Ke Xu and Weiqiang Wu
Sensors 2026, 26(18), 5715; https://doi.org/10.3390/s26185715 - 9 Sep 2026
Viewed by 95
Abstract
Millimeter wave (mmWave) Massive MIMO is a cornerstone technology for sixth-generation (6G) wireless networks, providing the directional gain necessary to overcome high path loss. However, the acquisition of high-fidelity Channel State Information (CSI) and the associated beamforming overhead remain significant bottlenecks, particularly in [...] Read more.
Millimeter wave (mmWave) Massive MIMO is a cornerstone technology for sixth-generation (6G) wireless networks, providing the directional gain necessary to overcome high path loss. However, the acquisition of high-fidelity Channel State Information (CSI) and the associated beamforming overhead remain significant bottlenecks, particularly in dynamic environments with frequent blockages. In this paper, we propose a fast and robust beamforming strategy enabled by a digital twin (DT) framework. Specifically, we develop a Conditional Generative Adversarial Network (cGAN)-based DT module that serves as a high-fidelity virtual surrogate for site-specific ray-tracing. By processing environmental 3D geometry and dynamic obstacle data, the cGAN predicts real-time Beam-Power Maps (BPM) with minimal computational latency. Building upon these predictions, we introduce a Graph Neural Network (GNN)-based resource allocation agent that models the network as a spatial interference graph to perform coordination and power control. Numerical results demonstrate that our proposed DT-assisted approach significantly reduces online interaction overhead by shifting the computational burden of ray-tracing to an offline generative phase. Furthermore, the framework achieves superior sum-rate performance and link robustness under dynamic blockages compared to conventional deep learning and heuristic benchmarks. Full article
(This article belongs to the Special Issue Advanced B5G/6G Communications)
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26 pages, 923 KB  
Article
Multi-Filter Quantum Neural Networks for Efficient Channel Estimation in RIS-Assisted Systems
by Min-Hyeok Choi, Ja-Eun Kim, Seung-Han Kim, Myung-Sun Baek, Gyeong-Ho Lee, Duck-Dong Hwang and Hyoung-Kyu Song
Sensors 2026, 26(13), 4249; https://doi.org/10.3390/s26134249 - 4 Jul 2026
Viewed by 382
Abstract
A reconfigurable intelligent surface (RIS) is a promising technology for beyond-fifth-generation (B5G) and sixth-generation (6G) wireless communications, but its passive reflection and two-hop double-fading structure make cascaded channel estimation challenging. Conventional convolutional neural network (CNN) estimators require many trainable parameters, while a single [...] Read more.
A reconfigurable intelligent surface (RIS) is a promising technology for beyond-fifth-generation (B5G) and sixth-generation (6G) wireless communications, but its passive reflection and two-hop double-fading structure make cascaded channel estimation challenging. Conventional convolutional neural network (CNN) estimators require many trainable parameters, while a single shallow parameterized quantum circuit (PQC) may have limited feature representation. Deep quantum circuits can also suffer from noise and barren-plateau effects on noisy intermediate-scale quantum (NISQ) devices. To address these issues, this paper proposes a multi-filter quantum convolutional neural network (MF-QCNN) for cascaded channel estimation in RIS-assisted multi-user uplink systems. The proposed model uses multiple independent shallow PQC filters in parallel, concatenates their measured features, and estimates the cascaded channel through a compact classical dense head, with the total trainable-parameter count scaling as 182F+696 for F parallel filters. Simulation results, compared with a single-filter quantum convolutional neural network (QCNN), CNN, and multilayer perceptron (MLP) baselines, show that at a signal-to-noise ratio (SNR) of 20 dB, the 3-filter MF-QCNN reduces the normalized mean squared error (NMSE) by approximately 22.9, 8.1, and 4.6 dB relative to the single-filter QCNN, CNN, and MLP baselines, respectively, while using only about 19.3% of the CNN trainable parameters. Under zero-forcing (ZF) precoding, it achieves the highest achievable sum rate among the learning-based estimators; at SNR = 30 dB, it improves the achievable sum rate by approximately 17.4% and 12.8% over the CNN and MLP baselines, respectively. These simulation results suggest that the parallel shallow-PQC design can serve as a compact quantum-aided estimator for RIS channel estimation and may provide a useful basis for future studies on AI-native transceiver design in B5G/6G networks. Full article
(This article belongs to the Special Issue Advanced B5G/6G Communications)
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