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The Key Technologies for Wireless Communication, Computing and Sensing
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The Key Technologies for Wireless Communication, Computing and Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: 25 October 2026 | Viewed by 745

Editor

Dr. Xiaoyan Hu

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an 710049, China
Interests: 6G; edge computing; reconfigurable intelligent surface; UAV communications; physical layer security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rapid development of wireless networks, the technologies of wireless communication, computing, and sensing have become foundational pillars of 6G intelligent applications such as autonomous driving and smart cities. While their integration is a core trend, standalone advancements in each domain remain critical—communication enables reliable transmission, computing powers data processing, and sensing provides environmental perception. Emerging technologies like reconfigurable intelligent surface (RIS) and unmanned aerial vehicles (UAVs) offer transformative potential, addressing challenges such as path loss, limited coverage, and resource constraints. Both independent innovation and cross-domain integration are vital to unlocking future wireless systems’ full potential.

This Special Issue aims to collate cutting-edge research on key technologies for wireless communication, computing, and sensing—encompassing standalone advancements, integration, and empowerment via RIS/UAVs. We welcome the submission of original contributions on theories, algorithms, and experiments. Topics of interest include, but are not limited to, the following:

1. Wireless Communication:

  • URLLC/mMTC technologies and advanced modulation/waveform design (e.g., OTFS);
  • mmWave/THz/VLC systems with RIS/UAV enhancement;
  • Massive MIMO and physical layer security for RIS/UAV-aided networks.

2. Wireless Computing:

  • Edge/cloud offloading and resource scheduling for UAV/edge nodes;
  • Lightweight computing frameworks and AI-driven optimization;
  • RIS-assisted computing task partitioning and latency reduction.

3. Wireless Sensing:

  • High-precision radar/LiDAR/VLP with UAV/RIS augmentation;
  • Low-power sensing hardware and multi-sensor fusion;
  • Anti-interference signal processing for dynamic sensing scenarios.

4. Integration and Emerging Technologies:

  • ISAC waveform design and joint optimization with RIS/UAVs;
  • UAV swarm collaborative communication–computing–sensing;

RIS-metasurface co-design with 6G key technologies.

Prof. Dr. Xiaoyan Hu
Guest Editor

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

  • wireless communication, edge and cloud computing
  • integrated communication and sensing
  • RIS
  • UAV

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Published Papers (2 papers)

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Research

31 pages, 3483 KB  
Article
Joint Quality–Reliability Analysis of IRS-Assisted Communications in Presence of Inverse Power Lomax Fading Channel
by Aleksey S. Gvozdarev and Roman Yu. Manakhov
Sensors 2026, 26(16), 5159; https://doi.org/10.3390/s26165159 - 14 Aug 2026
Abstract
In this work, we study the joint performance of the quality and reliability in terms of quality–reliability (JQR) performance of an intelligent reflecting surface (IRS)-assisted wireless communication system under severe multipath fading and shadowing. The wireless channel model is given by the Inverse [...] Read more.
In this work, we study the joint performance of the quality and reliability in terms of quality–reliability (JQR) performance of an intelligent reflecting surface (IRS)-assisted wireless communication system under severe multipath fading and shadowing. The wireless channel model is given by the Inverse Power Lomax (IPL) fading model, representing a heavy-tailed fading channel, which can describe the hyper-Rayleigh fading and is verified using two different experimentally obtained measurement scenarios, namely, the LTE-case for high-frequency, long-range cellular communications and the device-to-device (D2D) case for lower-frequency short-range communications. For the considered channel model and communication scheme, analytical expressions for the outage probability (a metric related to the reliability) and the average bit error rate for both coherent and non-coherent modulation schemes (metrics associated with the quality of the communication system) are provided. By combining the aforementioned expressions, a unified JQR curve, together with its asymptotic forms in the high signal-to-noise ratio regime and asymptotically large number of IRS elements, is derived. It is proved analytically that the use of IRS with infinite elements can remove fading, while for a finite number of IRS elements, a closed-form signal-to-noise ratio (SNR) penalty factor is presented. The numerical analysis demonstrates that coherent modulations outperform non-coherent ones, higher-order quadrature amplitude modulation (QAM) systems are highly sensitive to the multipath fading, and the LTE-case exhibits better performance compared to the D2D-case for equal settings. Moreover, the joint quality–reliability approach highlights the existence of regions where quality is more preferable than reliability, allowing the allocation of resources based on these regions. All expressions have been verified using Monte Carlo simulations with excellent agreement. Full article
(This article belongs to the Special Issue The Key Technologies for Wireless Communication, Computing and Sensing)
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26 pages, 3464 KB  
Article
Digital Twin-Enabled Dynamic Aggregation for Efficient Federated Learning
by Wenqin Zhuang, Yuao Wang and Guocheng Wang
Sensors 2026, 26(14), 4460; https://doi.org/10.3390/s26144460 - 14 Jul 2026
Viewed by 347
Abstract
Federated learning (FL) enables collaborative model training without sharing raw data, but it faces challenges due to client heterogeneity, leading to inefficiency and reduced accuracy. This paper proposes a digital twin (DT)-based dynamic FL aggregation method to address these issues. The framework integrates [...] Read more.
Federated learning (FL) enables collaborative model training without sharing raw data, but it faces challenges due to client heterogeneity, leading to inefficiency and reduced accuracy. This paper proposes a digital twin (DT)-based dynamic FL aggregation method to address these issues. The framework integrates a DT layer on the server side to perform preaggregation evaluations, simulating various aggregation strategies to select the optimal approach before actual global aggregation. An adaptive clustering method based on K-means is employed to group clients with similar characteristics, and a hierarchical aggregation evaluation strategy is designed to optimize both intra-cluster and inter-cluster aggregation, with the goal of minimizing latency and energy consumption while maximizing model accuracy. Simulation results on the MNIST and CIFAR-10 datasets demonstrate that the proposed method not only accelerates model convergence and improves accuracy but also significantly reduces training latency and energy consumption costs compared with baseline FL algorithms. This DT-assisted approach delivers a practical and effective optimization solution for federated learning deployment over large-scale heterogeneous IoT sensor networks. Full article
(This article belongs to the Special Issue The Key Technologies for Wireless Communication, Computing and Sensing)
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