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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 11312

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Special Issue Editors

Prof. Dr. Peter Han Joo Chong

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Guest Editor

Department of Electrical and Electronic Engineering, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
Interests: mobile computing; wireless communication systems and technologies; networking and communications; communications engineering; cyber–physical systems and the Internet of Things; 4/5/6G; vehicular networks; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Dr. Kalupahana Liyanage Kushan Sudheera

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Guest Editor Assistant

Department of Electrical and Information Engineering, Faculty of Engineering, University of Ruhuna, Galle 80000, Sri Lanka
Interests: software-defined networking; vehicular networks; cyber security; data mining; machine learning; artificial intelligence; Internet of Things; optimization; software engineering

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Communications Section is compiling a collection of papers submitted exclusively by Editorial Board Members (EBMs) of our section and outstanding scholars in this research field.

The purpose of this Special Issue is to publish a set of papers that typify the very best insightful and influential original articles or reviews in which our section EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed book edition following the deadline, and they will be extensively promoted.

We wish to take this opportunity to call on more excellent scholars to join the Communications Section so that we can achieve more milestones together.

The topics of interest to this Special Issue include, but are not limited to, the following:

Joint radar and communications;
Wireless, mobile, Ad Hoc, and sensor networks;
Integrated positioning and communications;
Integrated sensing and communications;
RF sensing and localization;
Mobility, sensing, and networking;
Reconfigurable intelligent surfaces for sensing and communications;
Sensor-aided communication in vehicular channels;
Cloud, edge, and fog computing for sensing and inference;
Sensing, communication, and networking in challenging scenarios (e.g., in-body networks, underground, underwater, rural and low-income areas, and space);
Trustworthiness of mobile, wireless, and sensor systems;
Notable 5G/6G communication systems;
RFID (Radio Frequency Identification);
Antenna arrays;
Distributed sensing and communications;
Sensor networks and data communications;
Real-time communications in wireless sensor networks.

Prof. Dr. Peter Han Joo Chong
Guest Editor

Dr. Kalupahana Liyanage Kushan Sudheera
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-blind 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
integrated sensing and communications
antenna technology
radio frequency identification
5G/6G

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Published Papers (10 papers)

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Research

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14 pages, 1556 KB

Open AccessArticle

Deep Learning-Based Dynamic Time Division ISAC Beamforming for Vehicular Networks

by Junseok Lim and Jaewoo So

Sensors 2026, 26(9), 2790; https://doi.org/10.3390/s26092790 - 30 Apr 2026

Viewed by 651

Abstract

Integrated sensing and communications (ISAC) is a promising key technology for vehicular networks, because it allows roadside units to support both data transmission and radar-like sensing over the same spectrum and hardware platform. In conventional time division ISAC systems, each frame is divided into sensing and communication phases with a fixed ratio, which determines the tradeoff between the sensing accuracy and the communication throughput. However, in high-mobility vehicular environments, a fixed sensing–communication split is often suboptimal due to time-varying channel and intervehicle interference variations. In this paper, we propose a dynamic sensing–communication time division and ISAC beamforming scheme that minimizes the Cramér–Rao lower bound while satisfying the minimum effective communication sum rate. We further develop a deep reinforcement learning framework based on proximal policy optimization to find the optimal time division ratio and beamforming vectors. Simulation results show that the proposed dynamic time division beamforming scheme significantly outperforms the conventional fixed time division beamforming schemes in terms of sensing accuracy and the communication sum rate. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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16 pages, 2852 KB

Open AccessArticle

Wideband MIMO Antenna System Employing Slot and Via Loading Technique for 5G Terminals

by Xin-Hao Ding, Liang-Jun Zhan, Zhen Tan and Shah Nawaz Burokur

Sensors 2026, 26(9), 2745; https://doi.org/10.3390/s26092745 - 29 Apr 2026

Viewed by 439

Abstract

This work introduces a wideband four-element multiple-input multiple-output (MIMO) antenna system with four rectangular patches arranged in a sequentially rotated configuration. Wideband frequency operation is realized by exploiting the TM₁₀, TM₀₁ and TM_11δ modes through the utilization of a slot and metallized vias in the patch design. Another group of metallized vias are used to control coupling between the antenna elements, achieving an isolation level of over 17 dB. A prototype is fabricated and measured, demonstrating −6 dB impedance bandwidth ranging from 4.23 GHz to 5.96 GHz, enabling coverage of the N79 (4.4–5 GHz), V2X (5.905–5.925 GHz) and Wi-Fi 5/6 (5.150–5.850 GHz) frequency bands. The MIMO antenna features an efficiency of over 45% and a low envelope correlation coefficient (ECC) lower than 0.25. Owing to its broad bandwidth, compact geometry, and good isolation, the proposed MIMO antenna provides an efficient and practical solution for 5G MIMO applications integrated within mobile terminal back covers. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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31 pages, 1361 KB

Open AccessArticle

Ground User Clustering for Adaptive Multibeam GEO Satellite Networks

by Heba Shehata, Hazer Inaltekin and Iain B. Collings

Sensors 2026, 26(8), 2384; https://doi.org/10.3390/s26082384 - 13 Apr 2026

Viewed by 465

Abstract

This paper considers geometry-aware ground user clustering and Cluster Center Optimization for beam pointing and scheduling in adaptive multibeam Geostationary Earth Orbit (GEO) satellite networks. By grouping ground users, beams can be directed toward user clusters to maximize satellite throughput. We propose GeoClust, a polynomial-time geometric user clustering algorithm for adaptive multibeam GEO satellite networks, using a geometric set-cover approach that explicitly balances link signal-to-interference-plus-noise ratio (SINR) and hopping overhead. The algorithm employs a Boyle–Dykstra projection-based cluster center update within an alternating optimization framework, combined with nearest-center membership updates, to enforce the cluster-radius constraint while ensuring feasibility and provable convergence. It also achieves near-linear throughput scaling with increasing number of RF chains. Numerical evaluations on real-world population data show that, under heavy traffic conditions, our approach more than doubles the zero outage and median user rates compared to benchmark schemes. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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15 pages, 2805 KB

Open AccessArticle

Relay-Assisted Communications over Multi-Cluster Two-Wave Fading Channels

by Muhammad Junaid Rabbani, Zakir Hussain, Haider Mehdi, Shahzad Ashraf and Syed Muhammad Atif Saleem

Sensors 2026, 26(5), 1702; https://doi.org/10.3390/s26051702 - 8 Mar 2026

Viewed by 356

Abstract

This paper examines the secrecy performance of a decode-and-forward (DF) relay-assisted device-to-device (D2D) communication system operating over Terahertz (THz) channels in multi-cluster two-wave (MTW) fading environments. Eavesdroppers are located near the relay and the receiver, intercepting their respective signals. Co-channel interference (CCI) affecting the relay, receiver, and eavesdroppers is also considered. To counter fading, both the relay and the receiver employ Maximal Ratio Combining (MRC). The analysis uses a characteristic function (CF)-based approach to derive key secrecy metrics, such as secrecy outage probability, secrecy success probability, the probability of strictly positive secrecy capacity, and intercept probability. The derived expressions are dependent on the characteristics of the THz, MTW fading, and CCI parameters. Finally, the system’s performance is then evaluated numerically for a range of channel and interference parameters. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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24 pages, 4158 KB

Open AccessArticle

Federated Learning and Data Mining-Based Botnet Attack Detection Framework for Internet of Things

by Kalupahana Liyanage Kushan Sudheera, Lokuge Lehele Gedara Madhuwantha Priyashan, Oruthota Arachchige Sanduni Pavithra, Malwaththe Widanalage Tharindu Aththanayake, Piyumi Bhagya Sudasinghe, Wijethunga Gamage Chatum Aloj Sankalpa, Gammana Guruge Nadeesha Sandamali and Peter Han Joo Chong

Sensors 2026, 26(5), 1573; https://doi.org/10.3390/s26051573 - 2 Mar 2026

Viewed by 569

Abstract

Botnet attacks in Internet of Things (IoT) environments often occur as multi-stage campaigns, making early and reliable detection difficult across distributed and privacy-sensitive networks. Centralized detection approaches are often limited by heterogeneous traffic characteristics, severe data imbalance, and the need to aggregate large volumes of raw network data, raising scalability and privacy concerns. To address these challenges, this paper proposes FDA, a federated learning-based and data mining-driven framework for stage-aware botnet attack detection in IoT networks. FDA operates at network gateways, where anomalous traffic is first detected and then abstracted into compact and interpretable patterns using Frequent Itemset Mining (FIM). This pattern-based representation reduces noise and local traffic bias, enabling more robust learning across different IoT networks. Lightweight neural network models are trained locally at gateways, and a global model is learned through federated aggregation of model parameters, avoiding direct sharing of raw network data while enabling gateways to collaboratively learn evolving attack patterns across different IoT networks. Experimental results show that FDA achieves anomaly detection F1-scores above 99% across all gateways and multi-stage botnet attack classification F1-scores in the range of 48–49%, which are comparable to centralized machine-learning baselines while operating under decentralized and privacy-preserving constraints. Overall, FDA provides a practical, privacy-preserving, and effective solution for distributed botnet attack stage detection in real-world IoT deployments. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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20 pages, 4053 KB

Open AccessArticle

Higher-Order Markov Model-Based Analysis of Reinforcement Learning in 6G Mobile Retrial Queueing Systems

by Djamila Talbi and Zoltan Gal

Sensors 2025, 25(23), 7245; https://doi.org/10.3390/s25237245 - 27 Nov 2025

Viewed by 1024

Abstract

The dynamic behavior of the retrial queueing system following the incorporation of Deep Q-Network Reinforcement Learning in 6G mobile communication services is examined in this study. The proposed method lies in analyzing the DQN-RL agent’s learning convergence by using the first- and second-order Markov chain method. By simulating the temporal evolution of reward sequences as Markov and second-order Markov chains, we can quantify convergence characteristics through mixing time analysis. To capture a wide operational landscape, a thorough simulation framework with 120 independent parameter combinations is created. The obtained results indicate that Markov chain analysis confirms 10 training episodes are more than sufficient for policy convergence, and in some cases, as few as 5 episodes allow the agent to enhance the mobile network performance while maintaining low energy consumption. To assess learning stability and system responsiveness, the mixing time of DQN RL rewards is calculated for every episode and configuration. A deeper understanding of the temporal dependencies in the reward process can be gained by incorporating higher-order Markov models. This paper concentrates on studying the learning convergence using an analysis of the Markov model’s spectral gap properties as an indicator. The results provide a rigorous foundation for optimizing 6G queueing strategies under uncertainty by highlighting the sensitivity of DQN convergence to system parameters and retrial dynamics. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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18 pages, 2214 KB

Open AccessArticle

AI-Native PHY-Layer in 6G Orchestrated Spectrum-Aware Networks

by Partemie-Marian Mutescu, Adrian-Ioan Petrariu, Eugen Coca, Cristian Patachia-Sultanoiu, Razvan Marius Mihai and Alexandru Lavric

Sensors 2025, 25(23), 7206; https://doi.org/10.3390/s25237206 - 26 Nov 2025

Cited by 1 | Viewed by 1751

Abstract

The evolution from fifth generation (5G) to sixth generation (6G) networks demands a paradigm shift from AI-assisted functionalities to AI-native orchestration, where intelligence is intrinsic to the radio access network (RAN). This work introduces two AI-based enablers for PHY-layer awareness: (i) a waveform classifier that distinguishes orthogonal frequency-division multiplexing (OFDM) and orthogonal time frequency space (OTFS) signals directly from in-phase/quadrature (IQ) samples, and (ii) a numerology detector that estimates subcarrier spacing, fast Fourier transform (FFT) size, slot duration, and cyclic prefix type without relying on higher-layer signaling. Experimental evaluations demonstrate high accuracy, with waveform classification achieving 99.5% accuracy and numerology detection exceeding 99% for most parameters, enabling robust joint inference of waveform and numerology features. The obtained results confirm the feasibility of AI-native spectrum awareness, paving the way toward self-optimizing, context-aware, and adaptive 6G wireless systems. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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28 pages, 1078 KB

Open AccessArticle

Performance Analysis of OCDM in ISAC Scenario

by Pengfei Xu, Mao Li, Tao Zhan, Fengkui Gong, Yue Xiao and Xia Lei

Sensors 2025, 25(17), 5481; https://doi.org/10.3390/s25175481 - 3 Sep 2025

Cited by 1 | Viewed by 1770

Abstract

The rapid evolution of communication systems, exemplified by the Internet of Things (IoT), demands increasingly stringent reliability in both communication and sensing. While Orthogonal Frequency Division Multiplexing (OFDM) struggles to meet the challenges posed by complex scenarios, Orthogonal Chirp Division Multiplexing (OCDM) has gained attention for its robustness and spectral efficiency in Integrated Sensing and Communication (ISAC) systems. However, its sensing mechanism remains insufficiently explored. This paper presents a theoretical analysis of the communication and sensing performance of OCDM waveforms within the ISAC framework. Specifically, a closed-form BER expression under equalization is derived, alongside the ambiguity function and detection performance evaluation under matched filter (MF) and Generalized Likelihood Ratio Test (GLRT) detectors with a constant false alarm rate (CFAR) criterion. Simulation results demonstrate that OCDM offers comparable sensing performance to OFDM while achieving superior communication robustness in complex environments. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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25 pages, 3053 KB

Open AccessArticle

Enhanced YOLOv11 Framework for Accurate Multi-Fault Detection in UAV Photovoltaic Inspection

by Shufeng Meng, Yang Yue and Tianxu Xu

Sensors 2025, 25(17), 5311; https://doi.org/10.3390/s25175311 - 26 Aug 2025

Cited by 5 | Viewed by 2578

Abstract

Stains, defects, and snow accumulation constitute three prevalent photovoltaic (PV) anomalies; each exhibits unique color and thermal signatures yet collectively curtail energy yield. Existing detectors typically sacrifice accuracy for speed, and none simultaneously classify all three fault types. To counter the identified limitations, an enhanced YOLOv11 framework is introduced. First, the hue-saturation-value (HSV) color model is employed to decouple hue and brightness, strengthening color feature extraction and cross-sensor generalization. Second, an outlook attention module integrated into the backbone precisely delineates micro-defect boundaries. Third, a mix structure block in the detection head encodes global context and fine-grained details to boost small object recognition. Additionally, the bounded sigmoid linear unit (B-SiLU) activation function optimizes gradient flow and feature discrimination through an improved nonlinear mapping, while the gradient-weighted class activation mapping (Grad-CAM) visualizations confirm selective attention to fault regions. Experimental results show that overall mean average precision (mAP) rises by 1.8%, with defect, stain, and snow accuracies improving by 2.2%, 3.3%, and 0.8%, respectively, offering a reliable solution for intelligent PV inspection and early fault detection. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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Review

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29 pages, 1200 KB

Open AccessReview

Surrogate-Based EM Design of RF and Microwave Components: A Systematic Review of Workflow Roles, Inverse Design, Multifidelity, and Active Learning

by Maria Prousali and Stelios Tsitsos

Sensors 2026, 26(8), 2504; https://doi.org/10.3390/s26082504 - 18 Apr 2026

Cited by 1 | Viewed by 582

Abstract

Surrogate models have been increasingly used to reduce the computational cost of electromagnetic (EM) design in RF and microwave components. However, component types, surrogate model families, and design workflows vary substantially across the literature. This systematic review provides a structured synthesis of surrogate-assisted EM design and optimization for RF and microwave applications. A Scopus-based screening process was employed to identify 180 journal articles published between 2012 and February 2026. After eligibility assessment, 126 studies were included in the final review corpus, whereas 54 were excluded. Six previous review articles were used separately for contextual positioning. The studies included were classified according to component category, surrogate model family, surrogate usage mode, inverse-design approach, multifidelity integration, active-learning adoption, and workflow function. The results showed that antennas and filters dominate the literature, whereas the Gaussian process or Kriging models and neural networks are the most frequent surrogate families. Optimization-based inverse design is the most commonly used, whereas multifidelity and active learning are less common. Overall, the included literature indicates that surrogate-assisted design is widely represented in RF and microwave design studies. However, no study in the included literature corpus has implemented a unified workflow that combines surrogate modeling, inverse design, multifidelity interaction, and active learning. Full article

(This article belongs to the Special Issue Feature Papers in Communications Section 2025–2026)

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