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In this article, we propose an event-triggered H∞ filtering algorithm that uses hysteresis-quantized measurements to achieve accurate state estimation in networked mass-switching autonomous marine vehicles (AMVs). The method tackles several key challenges commonly seen in networked AMV filtering, including limited communication bandwidth, restricted energy resources, and signal transmission delays.

We begin by establishing a dynamic model that captures the parameter variations caused by mass changes in AMVs. To mitigate bandwidth constraints and minimise signal chattering, we introduce a hysteresis-based quantization scheme. We further design an event-triggered mechanism that is both energy-efficient and capable of preventing excessively long intervals without triggering, thereby reducing the frequency of data transmission.

To enable rigorous stability analysis, a new augmented Lyapunov–Krasovskii functional is constructed, and Wirtinger-based inequalities are applied to manage the time-delay-dependent integral terms. Building on this foundation, we develop a co-design strategy that jointly determines both the filter and the event-triggering mechanism.

Simulation results show that the proposed method provides accurate state estimation for networked mass-switching AMVs while reducing the transmission rate by 75%, and it significantly outperforms conventional logarithmic quantizers in suppressing chattering.

Human Activity Recognition (HAR) plays a crucial role in intelligent healthcare, smart environments, and elderly monitoring. Traditional deep learning-based HAR methods often function as black-box models, limiting their interpretability. The recently proposed Kolmogorov-Arnold Network (KAN) utilizes explicit, mathematically defined basis functions, which clarify its operation and enhance interpretability. However, these methods still face challenges, such as slow training speed, high computational costs and suboptimal performance. Here we propose the Daily Activity Recognition with Optimized Wavelet-based KAN (DARKAN), a lightweight architecture that leverages wavelet decomposition to boost performance, and simplifies KAN structure to lower model parameters and computational complexity. Specifically, low- and high-frequency inertial measurement unit (IMU) signals are extracted by a wavelet transform, while time-domain features are incorporated to enrich feature representation. Subsequently, the B-Spline is replaced by the wavelet function as the activation function in KAN (wav-KAN), and the network depth of wav-KAN is reduced to two layers. Finally, the optimized wav-KAN is utilized to classify daily activities by fusing the extracted time-frequency features. Extensive experiments on three open-source datasets demonstrate that DARKAN outperforms stat-eof-the-art methods, achieving 98.82%, 97.11% and 98.57% in classification accuracy respectively while reducing the number of model parameters by 1.45× and FLOPs by 4×.

The open nature of wireless communications renders unmanned aerial vehicle (UAV) communications vulnerable to impersonation attacks, under which malicious UAVs can impersonate authorized ones with stolen digital certificates. Traditional fingerprint-based UAV authentication approaches rely on a single modality of sensory data gathered from a single layer of the network model, resulting in unreliable authentication experiences, particularly when UAVs are mobile and in an openworld environment. To transcend these limitations, this paper proposes SecureLink, a UAV authentication system that is among the first to employ cross-layer information for enhancing the efficiency and reliability of UAV authentication. Instead of using single modalities, SecureLink fuses physical-layer radio frequency (RF) fingerprints and application-layer micro-electromechanical system (MEMS) fingerprints into reliable UAV identifiers via multimodal fusion. SecureLink first aligns fingerprints from channel state information measurements and telemetry data, such as feedback readings of onboard accelerometers, gyroscopes, and barometers. Then, an attention-based neural network is devised for in-depth feature fusion. Next, the fused features are trained by a multi-similarity loss and fed into a one-class support vector machine for open-world authentication. We extensively implement our SecureLink using three different types of UAVs and evaluate it in different environments. With only six additional data frames, SecureLink achieves a closed-world accuracy of 98.61% and an open-world accuracy of 97.54% with two impersonating UAVs, outperforming the existing approaches in authentication robustness and communication overheads. Finally, our datasets collected from these experiments are available on GitHub: https://github.com/PhyGroup/SecureLink_data.

Graph Neural Networks (GNNs) have demonstrated remarkable capabilities in handling graph data. Typically, GNNs recursively aggregate node information, including node features and local topological information, through a message-passing scheme. However, most existing GNNs are highly sensitive to neighborhood aggregation, and irrelevant information in the graph topology can lead to inefficient or even invalid node embeddings. To overcome these challenges, we propose a novel Space Gravity-based Graph Neural Network (Gravity-GNN) guided by Deep Reinforcement Learning (DRL). In particular, we introduce a novel similarity measure called ”node gravity”, inspired by the gravitational force between particles in space, to compare nodes within graph data. Furthermore, we employ DRL technology to learn and select the most suitable number of adjacent nodes for each node. Our experimental results on various real-world datasets demonstrate that Gravity-GNN outperforms state-of-the-art methods regarding node classification accuracy, while exhibiting greater robustness against disturbances.

In Vehicular Edge Computing (VEC), vehicles of fload computational tasks to Roadside Units (RSUs) equipped with edge servers to achieve efficient processing. Considering that vehicles switch connections between RSUs during highspeed movement, obtaining the state information of other RSUs is crucial for achieving global collaborative decision-making. However, frequent sharing of RSUs’ state data during the training of scheduling models may result in privacy leakage risks. To address this issue, we federally train a joint scheduling model for task offloading and resource allocation without the need for state sharing among RSUs. We prove that the proposed task offloading problem influenced by resource allocation is a strict multi-node non-cooperative potential game problem, and use the potential function as the reward function for MultiAgent Deep Deterministic Policy Gradient (MADDPG). Finally, we propose the Fed-MADDPG algorithm to find the equilibrium point of task offloading and apply the gradient descent method and the Lagrange multiplier method to maximize the average task completion rate among RSUs under constraints, ensuring the framework has optimal computational and transmission performance. We conduct simulation experiments using realworld datasets, and the results show that this method has superior performance compared to previous approaches.

In an era of rapidly increasing bandwidth demand and revolutionary wireless technologies, efficient spectrum management is essential. This paper proposes a novel multi-tier tokenisation approach for dynamic spectrum management. Using the concept of heterogeneous tokenisation of spectrum bands, we develop a decentralised blockchain-based framework that enables spectrum sharing among users.

The spectrum space is represented using multi-planes. The first plane consists of unique spectrum bands, which are converted into NFTs for long-term allocations. The second plane subdivides these NFT spectrum bands into smaller units for short-term usage by retail users through fungible tokens. These fungible tokens are dynamically traded and mapped using particle swarm optimisation (PSO) to manage demand and supply.

The paper presents formal models of the system entities, along with algorithms for multi-tier token creation, dynamic token trading, and demand–supply mapping using PSO. To enhance privacy, a zero-knowledge proof (ZKP)-based approach is employed for user authentication.

The proposed framework provides a secure, transparent, and scalable solution for spectrum management, addressing the limitations of traditional centralised methods. Simulation results demonstrate the effectiveness of the framework in supporting dynamic spectrum access while ensuring privacy and scalability.

6G applications rely on data-intensive AI models for network optimization. These demand a scalable and energyefficient framework to handle massive device networks with stringent latency requirements which current solutions struggle to support. Although reinforcement learning (RL) and split learning have matured to provide commercial solutions elsewhere. Current solutions in 6G have not used them systematically to achieve the sustainability goals. In this paper, we propose a three-layer framework that minimizes energy consumption of the communication system capable of handling large number of devices. The proposed solution uses RL agents at the edge layer to mathematically model the system and communicate to fog layer for aggregation. The aggregated feature maps are further communicated to cloud layer for global model training. We use split learning for communication and training, the learning at each device are communicated for global model creation effectively. Each edge device improves the overall RL model where system matures quickly consuming minimal energy. The proposed framework’s efficacy has been tested extensively for accuracy and scalability, in terms of energy consumption, latency and memory utilizations. The simulation results validate the claims of maturity in models across edge, fog and cloud levels.

Sensor-enabled distributed energy resources (DERs) provide various advantages, including a lower carbon footprint, yet effective management of millions of DERs is still an issue. Virtual power plants (VPP) can integrate several DERs into a unified operational digital twin to enable real-time monitoring, analysis, and control. VPP may utilize advanced solutions to improve operational efficiency by combining substantial measurement data from DERs. However, effectively managing the quantity and complexity of data flows, whether streaming data or high-impact low-frequency data, is essential in maintaining the performance of DERs at sustained levels. The vast amounts of diverse data generated from various DERs pose significant challenges for storage, processing, and resource management. This article proposes a comprehensive framework that employs a distributed big data cluster to ensure scalable and reliable data storage and utilizes a robust message broker system for efficient data queuing. In addition, we present innovative load-balancing strategies within the VPP digital twin system. A decision tree algorithm is implemented to calculate the forthcoming workload collected by various deployed sensors at various DERs. The required resources are identified per workload, and the numbers are forwarded to the orchestrator. The orchestrator scales up and down resources based on resource utilization suggested by the decision tree algorithm when the resources or nodes are insufficient to handle the sensor data, optimizing the utilization of computing resources. The framework also features a failure detection component that performs root cause analysis to provide actionable insights for system optimization. Experimental results show that this framework ensures high efficiency, reliability, and real-time operational capability in VPP digital twin by addressing critical challenges in data storage, streaming data analysis, and load balancing.

As the scale of data continues to grow exponentially, managing resource allocation and energy consumption in big data systems becomes increasingly complex and critical. Moreover, with big data systems, energy efficiency is more important daily. In cloud environments, it can be the determining factor between reduced costs and lowered environmental damage. This paper presents a deep learning-based framework for accurately predicting instant energy consumption in real-time and detecting anomalies of different sizes in big data clusters. We use SmartMonit to gather task execution and real-time infrastructure data. A Feedforward Neural Network (FNN) predicts energy consumption from CPU utilisation, memory usage, and task profiling research. The system will track any deviation from predicted consumption with root cause analysis (RCA) if there are significant anomalies. We also integrate an Autoencoder to identify straggler tasks and inefficient resource utilisation. Userdefined functions are next applied to examine these anomalies and try to detect the underlying reasons, like distributed data processing, locality of computation exploitation, or resource waste. Given the scale and heterogeneity of big data workloads, the system’s ability to dynamically adjust and optimise resource usage is essential for handling complex processing tasks. The experimental results prove that the proposed system effectively enhances resource allocation and decreases wasted energy.

With the widespread adoption of healthcare Internet of Things (IoT) devices, the need for effective intrusion and anomaly detection has become pivotal in ensuring network security. However, the optimization of machine learning (ML) and deep learning (DL) models for these detection tasks frequently necessitates extensive computational resources, adversely affecting both temporal and energy efficiency. This article introduces an automated ML (AutoML) framework specifically tailored to enhance anomaly detection models, with a strategic focus on energy efficiency throughout the optimization process. The process commences with data preprocessing, followed by feature selection employing a combination of recursive feature elimination (RFE) and SHapley Additive exPlanations (SHAP) to identify important features for anomaly detection. Subsequently, a baseline multilayer perceptron (MLP) neural network model is trained, and hyperparameter optimization is executed in a constrained search space to mitigate energy consumption. The framework produces optimized models, which are assessed based on accuracy and energy consumption at various checkpoints, with the models demonstrating inferior performance systematically excluded based on predefined accuracy or energy consumption objectives. Experimental outcomes reveal that the pipeline effectively balances detection performance with energy consumption, with certain cases showing minimal accuracy losses (less than 1%) accompanied by substantial energy savings (over 60%), presenting a sustainable and resource-efficient approach to anomaly detection in IoT systems.

Federated learning is a promising approach that enables collaborative machine learning (ML) in distributed environments, such as the Internet of Medical Things (IoMT) while preserving consumer privacy. It allows multiple consumers to collaboratively train a model using their own data, sharing only the locally trained model rather than the raw data. Most existing federated learning systems assume a high level of trust in participating nodes, which is unrealistic in real-world consumer-centric scenarios. Involving untrusted nodes can compromise the integrity of the training process and result in potential data breaches. To address these challenges, this paper presents REWARDCHAIN, a novel federated learning framework that leverages blockchain technology to ensure trust and accountability among untrusted IoMT consumers. By recording all model updates and client contributions on an immutable blockchain ledger, REWARDCHAIN allows auditing of the entire training process and attributing any malicious behaviour to specific nodes. Moreover, we design an incentive mechanism that evaluates contributions based on data quality and participant reputation. This system motivates participants to contribute high-quality data through a reputation-constrained reward allocation. Our evaluations show that REWARDCHAIN effectively balances trust, security, and model performance, facilitating a more secure and effective federated learning ecosystem.

In network security, the issue of imbalanced data, where certain classes are under-represented, poses a significant challenge for intrusion detection systems (IDS). Machine learning algorithms often prioritise the majority class, resulting in poor detection of minority classes and reduced overall model accuracy. Accurately identifying these rare intrusions is essential for predicting and mitigating emerging threats. This paper presents an advanced IDS designed for Open Radio Access Networks (O-RAN) to address class imbalance by employing Synthetic Minority Over-sampling Technique (SMOTE) variants and Generative Adversarial Networks (GANs). The system was evaluated on the CICEVSE2024 dataset and a real-world O-RAN dataset. Results show that SMOTE improved the accuracy of weaker classifiers such as Naive Bayes by up to 10% (from 53.9% to 64.0%), with corresponding increases in recall and F1-score. Ensemble methods like Random Forest and XGBoost maintained high accuracy ( 81%–89%) and benefited from balanced recall when synthetic data was applied. However, GAN-generated data (CTGAN) showed little to no improvement over baseline models, and in some cases, SMOTE reduced accuracy for classifiers such as Logistic Regression and Random Forest on the O-RAN dataset (by up to 30%). These results highlight that while SMOTE variants can significantly enhance minority class detection, especially for weaker classifiers, the utility of GANs remains limited in this context. Future work should therefore focus on improving GAN-based data quality, exploring hybrid deep learning approaches, and extending IDS to real-time and multi-class scenarios.

Open Radio Access Network (O-RAN) introduces xApps to enable flexible, near real-time control of RAN functions. However, managing multiple xApps dynamically, under rapidly changing network conditions, becomes significantly more challenging in IoT-driven environments, where massive numbers of low-power devices may simultaneously access the network, triggering phenomena such as wake-up storms. In this work, we showcase and compare Digital Twin-enhanced Multi-Agent Reinforcement Learning (MARL) strategies for intelligent xApp management in IoT-enabled O-RAN systems. We explore how Digital Twins can simulate large-scale, event-driven IoT device behaviors, enabling proactive optimization of xApp scheduling while reducing communication and synchronization overhead. We further examine advanced MARL approaches, such as Hierarchical Agent Policy Optimization (HAPPO), to address scalability and coordination challenges under ultra-dense, low-latency, and energy-constrained LPWAN protocols like NB-IoT and LTE-M. We define two system-level KPIs to evaluate the performance of RL schemes: Conflict Avoidance Efficiency and Coordination QoS Improvement. The proposed MARL-based approach significantly outperforms conventional single-agent RL methods in both coordination effectiveness and overall QoS delivery, while maintaining a reasonable model complexity suitable for practical deployment.

Formal verification is crucial for ensuring the robustness of security protocols against adversarial attacks. The Needham-Schroeder protocol, a foundational authentication mechanism, has been extensively studied, including its integration with Physical Layer Security (PLS) techniques such as watermarking and jamming. Recent research has used ProVerif to verify these mechanisms in terms of secrecy. However, the ProVerif-based approach limits the ability to improve understanding of security beyond verification results. To overcome these limitations, we re-model the same protocol using an Isabelle formalism that generates sound animation, enabling interactive and automated formal verification of security protocols. Our modelling and verification framework is generic and highly configurable, supporting both cryptography and PLS. For the same protocol, we have conducted a comprehensive analysis (secrecy and authenticity in four different eavesdropper locations under both passive and active attacks) using our new web interface. Our findings not only successfully reproduce and reinforce previous results on secrecy but also reveal an uncommon but expected outcome: authenticity is preserved across all examined scenarios, even in cases where secrecy is compromised. We have proposed a PLS-based Diffie-Hellman protocol that integrates watermarking and jamming, and our analysis shows that it is secure for deriving a session key with required authentication. These highlight the advantages of our novel approach, demonstrating its robustness in formally verifying security properties beyond conventional methods.

Recently proposed over-the-air (OTA) controllers enable spectrum-efficient data fusion and control of networked dynamical systems such as swarm of drones and smart-grids. By properly tuning wireless channels, control signals can be formed via electromagnetic superposition of nodes’ states transmitted on the same frequency, and received at the controller. However, the challenge of current OTA controllers lies in the insufficient optimization variables to compensate wireless channels for OTA control objectives. In this work, we leverage the massive channel tuning elements of a reconfigurable intelligent surface (RIS) to design a RIS optimized OTA controller for networked dynamical systems. We first formulate the RIS-assisted OTA control objective function and a non-convex minimax eigenvalue optimization problem. Then, an quadratic upper-bound of the maximum eigenvalue is found to approximate the original optimization problem into a convex form. A smart-grid case-study is explored to validate our proposed RIS-OTA controller. Simulation results illustrate better control performance of our proposed RIS-OTA controller than existing transmitter power allocation based OTA controller, showing the promising potentials of RIS-optimized OTA controller in practical networked dynamical systems.

Metasurfaces, composed of subwavelength-scale meta-atoms, offer extensive capabilities in electromagnetic (EM) wave manipulation. In this study, we present a dual-functional transmissive chiral metasurface (TCMS) designed to achieve dual-band circular dichroism (CD) at 12.62 GHz and 16.305 GHz within the Ku-band, with CD values of 0.5 and 0.4, respectively, alongside asymmetric transmission (AT). These functionalities are realized simultaneously for right-hand circularly polarized (RHCP) and left-hand circularly polarized (LHCP) incident waves. Simulation results show that the dual-band CD effect is driven by the structural asymmetry of the chiral metasurface. Compared to single-function metasurfaces, this TCMS offers enhanced performance and dual-functionality while maintaining compactness. We believe that this dual-band, dual-functional TCMS, with its simplified geometry, holds significant promise for applications in analytical chemistry, imaging, sensing, spectroscopy, satellite communication, and the development of advanced communication components that enable efficient polarization manipulation.

Circular dichroism (CD) in electromagnetic (EM) waves offers significant potential for advancing various scientific and technological fields. This study presents a unique approach using a C-shaped transmissive chiral metasurface, engineered to achieve strong CD within the X-band frequency range. The metasurface demonstrates outstanding selectivity for left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) incident waves, featuring enhanced bandwidth and a strong CD of 68.3%. With a thickness of 0.8 mm and built on an FR-4 substrate, the metasurface is optimized for high selectivity toward LHCP and RHCP waves, representing a significant advancement in the study of transmissive metasurfaces. By seamlessly combining theoretical knowledge with practical application, this research not only extends the boundaries of metasurface design but also paves the way for diverse applications across various scientific disciplines, including satellite communication, stealth technology, and antenna design, etc.

This paper presents a novel dual-mode chiral metasurface (CM) designed to achieve strong circular dichroism (CD) in both transmission and reflection mode within the Ku-band. The proposed dual-mode CM demonstrates CD i.e., an efficient conversion of linearly polarized (LP) electromagnetic (EM) waves into circularly polarized (CP) waves, both within a broader spectrum as well as at single frequencies in both transmission and reflection mode, exhibiting asymmetric transmission (AT) response. This is achieved through a judiciously designed unit cell structure, which eliminates the requirement for intricate supercell configurations or active circuitry. The metasurface comprises a circular ring structure embedded with intelligently placed angle-induced slot and a metallic strip, fabricated using the cost-effective FR-4 substrate. The structure on the front side of the dual-mode CM is replicated on the back side of the substrate with a 90° rotation to achieve a chiral configuration. Under the forward-propagating y-polarized incident wave, the dual-mode CM demonstrates the capability to convert LP wave into right-handed circularly polarized (RHCP) wave at 14.064 GHz. Additionally, in transmission mode, it converts LP waves to left-handed circularly polarized (LHCP) wave over a wider frequency range of 16.60 – 17.03 GHz with AT response. In reflection mode, the dual-mode CM converts LP wave into RHCP wave at 12.048 GHz when subject to x-polarized incident wave propagating in the backward direction.

This paper discusses a miniaturized multi-wideband tablet antenna having a compact size of 20 × 9.6 × 1.8 mm³. The antenna is modeled on FR-4 substrate with thickness of 0.8 mm. The antenna covers LTE 700/2300/2500/3500, GPS 1575.42 MHz, GSM 850/1800/1900, UMTS 2100, Bluetooth 2.4 GHz and 5.2/5.8 GHz WLAN bands. The wideband operation is achieved by a novel inverted-F type structure which is capacitively coupled with two λ/4 microstrip resonators. Impedance matching at lower bands is further improved by a high-pass matching circuit integrated with the antenna feed. The proposed antenna has reasonable gain over all covered bands, low specific absorption rate (SAR) and high radiation efficiency from 80-90% over all LTE and WLAN bands.

An optically transparent and flexible metasurface is proposed for the V-band applications. The metasurface uses a silver nanowire (AgNW) deposit on a transparent PEN substrate to achieve optical transparency of greater than 85 %. The proposed metasurface exhibits cross polarization conversion in reflection mode from 54.2 GHz to 70.7 GHz with an angularly stable field of view of up to 40°. The metasurface is also capable of dual-band linear-to-circular polarization conversion from 50 GHz to 52.7 GHz and 73.8 GHz to 80.8 GHz. Moreover, a L-blt phase resolution in reflection mode is achieved by 90° rotation of unit cell for cross polarization reflectarray applications. The proposed metasurface is simulated on CST Microwave Studio and features low insertion loss, high return loss, and a wideband polarization conversion ratio (PCR) of better than 90 %, making it a desirable candidate for V-band cross polarization applications.

In this study, a 1-bit phase reconfigurable unit cell (UC) is proposed for an X-band intelligent reflective surface (IRS). The UC is designed by integrating a PIN diode into a slotted copper patch. The maximum phase shift occurs at the design frequency of 9.5 GHz and the phase difference varies within 180∘±20∘ from 9 to 10 GHz in the X-band. A 16 ×16 IRS is designed and simulated using the proposed UC to verify gain and beam steering performance. The IRS has a gain of 20.5dBi at θ=0∘. At θ=±40∘ the gain is 18.6 dBi which validates good beam steering performance.

Circular dichroism (CD) in electromagnetic (EM) waves offers significant potential for advancing various scientific and technological fields. This study presents a unique approach using a C-shaped transmissive chiral metasurface, engineered to achieve strong CD within the X-band frequency range. The metasurface demonstrates outstanding selectivity for left-hand circularly polarized (LHCP) and right-hand circularly polarized (RHCP) incident waves, featuring enhanced bandwidth and a strong CD of 68.3%. With a thickness of 0.8 mm and built on an FR-4 substrate, the metasurface is optimized for high selectivity toward LHCP and RHCP waves, representing a significant advancement in the study of transmissive metasurfaces. By seamlessly combining theoretical knowledge with practical application, this research not only extends the boundaries of metasurface design but also paves the way for diverse applications across various scientific disciplines, including satellite communication, stealth technology, and antenna design, etc.

This paper presents a broadband circularly polar-ized (CP) antenna array for millimeter-wave (mmWave) applications. The overall size of the 1×4 antenna array is 28×23mm2 and covers the 28 GHz to 38 GHz bands. Each element of the antenna array consists of a wide square slot (WSS) with a T-shaped stub extended in the ground plane. The impedance matching and the CP bandwidth is improved by introducing asymmetric fractals in the ground plane. The antenna array elements are fed by a simple 4-way power divider network. The simulated and measured impedance bandwidth of the array is 49% (26.6 GHz to 42.9 GHz) and 49% (26.6 GHz to 42.4 GHz) respectively. The simulated and measured 3dB axial ratio (AR) bandwidth is 29.5% (34 GHz to 44 GHz) and 28.5% (35 GHz to 42GHz) respectively. The peak gain of 8.5-11.2 dBi is measured throughout the bandwidth. The proposed antenna array is suitable for commercial use in future 5G communication systems.

This paper presents the RF performance analysis of various designs of RF – MEMS shunt capacitive switches in coplanar configuration. The switches consist of slotted bridges of a variety of shapes such as circular, hexagonal, and rectangular. The design consisting of the rectangular slotted bridge exhibits the best performance with a maximum insertion loss of 0.08 dB, return loss of greater than 20 dB and isolation of more than 30 dB. The design has an operational frequency range from DC-40 GHz. The designs are modelled and simulated using High Frequency Structure Simulator (HFSS).

Human activity recognition (HAR) in indoor environments is essential for healthcare, elder care, and assisted living applications, especially in complex scenarios involving non-line-of-sight (NLoS) and long-range conditions. Traditional single-sensor HAR systems often struggle with accuracy and reliability in such environments. This study introduces the RFiDAR system, a novel fusion approach that combines radio frequency identification (RFID) and Radar technologies with an LSTM-variational autoencoder (LSTM-VAE) model to enhance HAR accuracy and reliability. The RFiDAR system applied data-level, feature-level, and decision-level fusion techniques to integrate temporal patterns from RFID and Radar data, facilitating the recognition of five distinct activities at varying distances. Results indicate that fusion methods, particularly feature-level fusion, significantly improve classification accuracy. For instance, feature-level fusion achieves up to 98.8% accuracy at 2 meters and 97.9% at 3 meters, outperforming single-sensor models by 5.3% and 6.2%, respectively. The RFiDAR system demonstrates superior performance in complex scenarios, offering reliable and cost-effective solutions for autonomous, long-term monitoring. The proposed approach has potential applications in healthcare and accessible IoT environments and demonstrates the innovative impact of multi-sensor fusion in developing safe, flexible, and inclusive technology.

This paper presents RadSpecFusion, a novel dynamic attention-based fusion architecture for multi-radar human activity recognition (HAR). Our method learns activity-specific importance weights for each radar modality (24 GHz, 77 GHz, and Xethru sensors). Unlike existing concatenation or averaging approaches, our method dynamically adapts radar contributions based on motion characteristics. This addresses cross-frequency generalization challenges, where transfer learning methods achieve only 11%–34% accuracy. Using the CI4R dataset with spectrograms from 11 activities, our approach achieves 99.21% accuracy, representing a 15.8% improvement over existing fusion methods (83.4%). This demonstrates that different radar frequencies capture complementary information about human motion. Ablation studies show that while the three-radar system optimizes performance, dual-radar combinations achieve comparable accuracy (24GHz+77GHz: 96.1%, 24GHz+Xethru: 95.8%, 77GHz+Xethru: 97.2%), enabling flexible deployment for resource-constrained applications. The attention mechanism reveals interpretable patterns: 77 GHz radar receives higher weights for fine movements (superior Doppler resolution), while 24 GHz dominates gross body movements (better range resolution). The system maintains 71.4% accuracy at 10 dB SNR, demonstrating environmental robustness. This research establishes a new paradigm for multimodal radar fusion, moving from cross-frequency transfer learning to adaptive fusion with implications for healthcare monitoring, smart environments, and security applications.

This paper addresses the problem of uniform random generation of vectors of values with a fixed sum, subject to upper and lower constraints on the individual component values. Solutions to this problem are used extensively in the generation of tasksets, specifically task utilization values, in support of the performance assessment of schedulability tests for real-time systems. This paper introduces a general-purpose solution in the form of an Inverse Volume Ratio Sampling method that is applicable provided that it is possible to determine the ratio of the volume below a given hyperplane to the total volume of the valid region in n-dimensional space, as demarcated by the constraints and the fixed sum. An efficient approach is derived for volume calculation using numerical convolution, thus instantiating the ConvolutionalFixedSum algorithm, which provides a user-specified level of precision, while scaling at O(n^3log(n)). A stringent uniformity test is developed, called the slices test, which is able to fully explore the extent of the valid region in each of the n dimensions. The slices test reveals that while the outputs of UUnifast and ConvolutionalFixedSum form uniform distributions, in some cases the outputs of prior state-of-the-art algorithms do not.

This paper provides a comprehensive understanding of data and information flow in Autonomous Vehicles (AVs) with a particular focus on authorisation and validation challenges across different data processing stages. It examines the stage-specific key challenges and reviews existing solutions based on the data flow stages: collection, transmission, processing, actuation and storage. The findings highlight critical gaps in current approaches and suggest future research directions to enhance AV data authorisation and validation.

This work develops a novel ontology-based framework for achieving personal data sovereignty, overcomes data migration challenges through a federation-based sovereignty approach, and provides more fine-grained control over personal data than current solutions. The implementation demonstrates the feasibility of personal data sovereignty in practice and validates practical deployment without extensive infrastructure modifications. 

This paper proposes a novel integration of noise modulation with an autonomous RIS, enabling zero-energy transmission by exploiting thermal noise for information encoding and interference for powering the RIS. A resource allocation strategy optimizes both energy harvesting and communication performance. Analytical and simulation results demonstrate its strong potential for sustainable 6G IoT.

In this letter, we experimentally present a customized hardware framework to implement contactless millimeter-wave (mmWave) vital sign sensing in the 60 GHz license-free industrial, scientific, and medical (ISM) band using Dynamic Metasurface Antenna (DMA). The proof-of-concept experiment highlights how mmWave RF sensing challenges can be addressed through the pattern diversity of a DMA-assisted setup, eliminating the need for commercial Wi-Fi routers or reflecting surfaces. The measured results demonstrate that mmWave signals can be precisely steered toward the human body, and analysis of the reflected signals enables accurate estimation of breathing and heart rates. The reflected signal strength exceeds ambient noise by over 30 dB, supporting reliable vital sign sensing over the mmWave channel. We experimentally evaluate breathing and heart rates using the magnitude signature of measured channel state information in a realistic indoor environment. Experimental results demonstrate low mean absolute errors for both respiration and heart rate compared to ground truth sensor data. The proposed DMA-assisted mmWave testbed unlocks the potential for the practical deployment of various mmWave sensing applications, such as fall detection, gait recognition, human localization and spatial tracking, and realtime vital sign monitoring in dynamic environments, as well as integrated sensing and communication.

The adoption of Generative Artificial Intelligence (GenAI) in Radio Access Networks (RAN) presents new opportunities for automation and intelligence across network operations. GenAI-powered agents, leveraging Large Language Models (LLMs), can enhance planning, execution, and decision-making for orchestration and real-time optimisation of 6G networks. Standardizing the implementation of the Agentic architecture for RAN is now essential to establish a unified framework for RANOps and AgentOps. One of the key challenges is to develop a blueprint that incorporates best practices for memory integration, tool generation, multi-agent orchestration, and performance benchmarking. This study highlights key areas requiring standardization, including agent tool specifications, RAN-specific LLM fine-tuning, validation frameworks, and AI-friendly documentation. We propose a dedicated research initiative on GenAI-for-RAN and GenAI-on-RAN to address these gaps and advance AI-driven network automation.

Reconfigurable intelligent Surfaces (RIS) and half-duplex decoded and forwarded (DF) relays can collaborate to optimize wireless signal propagation in communication systems. Users typically have different rate demands and are clustered into groups in practice based on their requirements, where the former results in the trade-off between maximizing the rate and satisfying fine-grained rate demands, while the latter causes a trade-off between inter-group competition and intra-group cooperation when maximizing the sum rate. However, traditional approaches often overlook the joint optimization encompassing both of these trade-offs, disregarding potential optimal solutions and leaving some users even consistently at low date rates. To address this issue, we propose a novel joint optimization model for a RIS- and DF-assisted multiple-input single-output (MISO) system where a base station (BS) is with multiple antennas transmits data by multiple RISs and DF relays to serve grouped users with fine-grained rate demands. We design a new loss function to not only optimize the sum rate of all groups but also adjust the satisfaction ratio of fine-grained rate demands by modifying the penalty parameter. We further propose a two-phase graph neural network (GNN) based approach that inputs channel state information (CSI) to simultaneously and autonomously learn efficient phase shifts, beamforming, and relay selection. The experimental results demonstrate that the proposed method significantly improves system performance.

To advance towards carbon-neutrality and improve the limited {performance} of conventional passive wireless communications, in this paper, we investigate the integration of noise modulation with zero-energy reconfigurable intelligent surfaces (RISs). In particular, the RIS reconfigurable elements (REs) are divided into two groups: one for beamforming the desired signals in reflection mode and another for harvesting energy from interference signals in an absorption mode, providing the power required for RIS operation. Since the harvested energy is a random variable, a random number of REs can beamform the signals, while the remainder blindly reflects them. We present a closed-form solution and a search algorithm for REs allocation, jointly optimizing both the energy harvesting (EH) and communication performance.

This work presents the design, fabrication, and over-the-air (OTA) characterisation of a high-performance millimetre-wave antenna array operating in the 5G FR-2 band. We propose a 1×8 linear series-fed patch array with optimised performance for both point-to-point and point-to-multipoint communication scenarios. Designed at 28 GHz, the antenna demonstrates high gain (~14 dBi), wide impedance bandwidth, and low sidelobe levels, making it suitable for dense urban deployments. The performance of the prototype was experimentally verified using a Compact Antenna Test Range (CATR), enabling precise beam pattern characterisation under realistic propagation conditions. The study also highlights the impact of array geometry on beam directionality and discusses the practical implications of integrating such antennas in 5G base stations and backhaul links.

Leaky wave antennas (LWAs) are a type of directional antenna known for their ability to scan beams by simply changing the operating frequency. This makes them attractive for some applications like frequency scanning radars and sensing. However, traditional leaky-wave antennas face a significant limitation known as beam squint, where the direction of the radiated beam shifts with changes in the operating frequency. This frequency-dependent behaviour is undesirable in many wireless applications where bandwidth is limited and wideband mmWave circuits are expensive and complex to implement. and poses a major challenge in achieving consistent beam steering at a fixed frequency.

These limitations have sparked interest in more advanced alternatives, such as programmable metasurface antennas. To overcome these limitations, the team introduced a metasurface antenna solution that employs a leaky-wave radiation mechanism through specially engineered materials, enabling electronic beam steering at a fixed frequency, without relying on bulky or power hungry phase shifters. This intelligent antenna leverages reconfigurable meta-elements, allowing it to generate multiple beam patterns at a fixed operating frequency through the digital coding of its constituent elements. Such a fixed frequency beamsteering mechanism is not readily available in conventional LWAs.

Tested at the 60 GHz mmWave band, the proposed antenna demonstrates superior beamforming capabilities at a fixed operating frequency compared to conventional slot-based LWAs. It supports a wide range of applications, including point-to-point and multi-point high-speed wireless communication, short-range radar systems, next-generation 6G networks, and advanced healthcare sensing technologies.

We present RFSensingGPT, an integrated framework for radio frequency (RF) sensing that combines technical question-answering, code retrieval, and spectrogram analysis through retrieval-augmented generation (RAG). Our framework addresses the fundamental challenge of applying large language models to RF sensing applications, where specialized domain knowledge is underrepresented in general training corpora.

Jinsheng Yuan at Cranfield University gave a talk on how mixed computation precision for over-the-air federated learning, titled “Mixed-Precision Over-The-Air Federated Learning via Approximated Computing,” at the IEEE Wireless Communications and Networking Conference (WCNC) in March 2025. Here, the work maps the tradeoff between efficiency and federated accuracy whilst enabling modulation space super-position over the air for different resolution constellations.

At the IEEE International Conference on Machine Learning and Autonomous Systems (March 2025), Prof. Weisi Guo presented a talk titled “Contextualized Autonomous Drone Navigation using LLMs Deployed in Edge-Cloud Computing.” His work demonstrates how large language models (LLMs) can be deployed within 6G Open RAN networks to autonomously generate mission code for drones. This innovation highlights the potential of future 6G infrastructure as a command-and-control centre for aviation.

This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions and user experiences, especially in the context of 6G systems. It highlights the development and deployment of Large Telecom Models (LTMs), which are tailored AI models designed to address the complex challenges faced by modern telecom networks. The paper covers a wide range of topics, from the architecture and deployment strategies of LTMs to their applications in network management, resource allocation, and optimization. It also explores the regulatory, ethical, and standardization considerations for LTMs, offering insights into their future integration into telecom infrastructure. The goal is to provide a comprehensive roadmap for the adoption of LTMs to enhance scalability, performance, and user-centric innovation in telecom networks.

This paper addresses how the increasing demand for machine learning (ML) technologies has led to a significant rise in energy consumption and environmental impact, particularly within the context of distributed computing environments like the Edge-Fog-Cloud Continuum. This paper addresses the critical challenge of optimizing ML processes not only for performance but also for sustainability by introducing a novel Green Automated Machine Learning (AutoML) framework. The proposed framework integrates energy-aware task allocation into the AutoML pipeline, strategically distributing computational tasks across edge, fog, and cloud layers to minimize energy usage and carbon emissions without compromising model accuracy. To achieve this, the framework incorporates real-time monitoring and dynamic task allocation based on energy consumption, latency, and carbon footprint, utilizing a hierarchical approach that leverages the unique strengths of each layer within the continuum. The framework is supported by mathematical models that quantify energy consumption, communication latency, and environmental impact, offering a comprehensive metric for evaluating the sustainability of ML deployments. Through extensive simulations and real-world experiments, the framework demonstrates substantial improvements in energy efficiency and significant reductions in environmental footprint compared to traditional AutoML approaches. This research contributes to the advancement of sustainable AI by providing a practical solution for deploying ML models in a manner that balances performance with environmental responsibility.

In this paper, we propose an improved Transmission Control Protocol (TCP) algorithm called HYBIC, building upon existing CUBIC and HYBLA algorithms. This HYBIC algorithm is designed for improving capacity utilisation and transmission rate of heterogeneous networks such as the Integrated Satellite-Terrestrial Networks (ISTN) and long delay High Throughput Satellites (HTS) networks on Geostationary Earth Orbit that are characterised with high bandwidth-delay product path. Results analysed indicated that better performance is achieved using the proposed HYBIC algorithm. Considering the results, HYBIC achieved better performance in terms of window growth of 23×10 3 segments, transmission rate of 3Gbps, and capacity utilisation of 60 % compared with both CUBIC and HYBLA. However, the proposed HYBIC inherits the features of Round-Trip Time fairness, scalability, and friendliness of HYBLA and CUBIC algorithms.

In this work, we developed and proposed a real testbed with Integrated Satellite-Terrestrial Network (ISTN) scenario. This topology was used to measure the actual parameters that were used as the Smart Grid (SG) Quality of Service (QoS) metrics. Performance was evaluated with reference to the QoS requirements of SG applications. The emergence of new and evolving technologies, such as smarter energy utilities enabled by advanced communications technologies, necessitated evolutionary enhancements of both satellite and terrestrial communications systems. These would help improve energy efficiency and sustainability through the effective acquisition and analysis of data from energy systems. Hybrid communication technologies can be used to collect and share energy data efficiently and ubiquitously from generation stations to consumption sites. Thus, a topology using Inmarsat-4 satellites is presented that connects a robust, portable, and energy-efficient Broadband Global Area Network ground terminal. Performance evaluation was performed using measured parameters from the real ISTN topology testbed against key SG applications. The latency and bandwidth requirements for the 80-90% key SG applications were found to be within the QoS requirements range.

This work is part of Pillar 3 – Building Trustworthy Systems and examines the impact of customising the endorsement policy (EP) in Hyperledger Fabric (HLF) for data sharing in Autonomous Vehicles (AVs). In HLF, the EP can be tailored for each application, specifying the required approvals from the endorsing peers of participating organisations. Customisation is often essential to ensure that the transaction validation process aligns with the application’s specific security and business requirements. In our AV data-sharing platform, we implemented both default and customised EP configurations to assess their effectiveness.

In their latest paper, Dr. Yun Tang and Prof. Weisi explore the core of automated network and AI service provisioning, which relies on two key components:

1. Comprehension intelligence – This component understands both the use case requirements of end users and the (potentially dynamic) availability of networking (and AI) resources within the network (e.g., RAN and MEC).

2. Decision-making intelligence – This component monitors and configures the telecom network to deploy on-demand networking (and AI) services using RAN and MEC resources.

By adopting O-RAN, we gain the capability to monitor and configure RAN functions. Furthermore, by leveraging the intelligence of large language models (LLMs) equipped with use case and network knowledge through a RAG database, we enhance our ability to understand users’ needs and the network’s status, enabling automated network configurations and controls. This approach makes the vision of automated network (and AI) service provisioning feasible.

This paper outlines our initial step toward realizing this blueprint. It focuses on understanding and translating user-provided use case descriptions into actionable network specifications using RAG-LLMs. To advance this initiative, we propose establishing a use case knowledge database through crowd-sourcing of potential or real-life 6G use cases from the public. This database will be accessible to the public and integrated into the LLMs to support the entire network service automation pipeline.

Stay tuned as we work on integrating our current implementation with open-source O-RAN projects and 5G simulators!

This paper introduces the Search-Voxel Ellipse Normalisation technique designed to enhance the accuracy and reliability of respiratory rate monitoring using MIMO FMCW radar in environments impacted by metal-induced multipath distortions. The key contributions include the implementation of an ellipse normalisation method that effectively reduces noise and phase distortions, and the introduction of a voxel selection policy that integrates the Least Squares Method with Dynamic Time Warping and K-Nearest Neighbours to exclude low SNR voxel and refine signal quality. Additionally, the adoption of a Gaussian estimator offers a robust mechanism for continuous and precise respiratory rate estimation. Together, these innovations significantly enhance the efficacy of radar-based health monitoring systems, especially in complex indoor environments.

The aviation and air mobility sectors are undergoing rapid transformation, driven by 6G advancements and the need for secure and reliable airborne communication systems. The integration of Unmanned Aerial Systems (UAS) into commercial and logistical operations is redefining air mobility. The Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol enhances UAS communication safety by minimizing data transmission collisions and ensuring efficient communication. Combining CSMA/CA with Sixth Generation (6G) technology further strengthens UAS operations’ reliability and effectiveness. MATLAB simulations provide insights into signal quality and latency as a function of distance, critical for analysing safety communications. Additionally, understanding handover procedures, especially vertical handovers in UAS operations, is vital for public safety and the development of advanced Unmanned Traffic Management (UTM) and Air Traffic Management (ATM) systems.

This new paper ‘Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation’ demonstrates that Over-the-Air Federated Learning (OTA-FL) offers numerous advantages in challenging environments, including ultra-high radio resource efficiency and enhanced privacy preservation. It achieves this by combining client updates through the superposition of modulated waveforms in the propagation channel. Traditional research in OTA-FL assumes that the FL clients have the same computation bit resolutions (e.g., 4-bit, 8-bit 16-bit…etc.), and differential approximate computing (AxC) bit resolution updates pose a problem when reflected into the modulation constellation space superposition (e.g., QPSK, 8-PSK, 16-QAM). Our paper is due to appear in 2025 IEEE Wireless Communications and Networking Conference (WCNC) develops 2 innovations: (1) to create a common modulation basis for multi-resolution OTA-FL, and (2) to create a performance vs. energy efficiency trade-off mapping by adapting the client side bit-resolution. Our work paves the way for Auto-ML: the automatic orchestration of resources in distributed 6G and IoT computing architectures.

The work presented in this paper addresses the risk of misconfiguration introduced by logical inconsistencies when trying to balance QoS, for example between service availability and power efficiency, during the adoption of Open Radio Access Networks (O-RAN) and the deployment of AI/ML-driven applications (xApps) to enhance and simplify network management. This work proposes an approach to use probabilistic model checkers (PRISM and Storm) to quantitatively compute optimal thresholds between energy efficiency and service availability for a defined policy in a given scenario with three radio cells (RCs) and nine user equipments (UEs) while UEs are dynamically switched on and off under uncertainty. As a result, the work can prevent logical inconsistencies in xApp development and safeguard AI-driven applications to ensure network decisions are logically consistent, enhancing reliability and preventing misconfigurations.

The work presented in this paper addresses the accessibility issue of formal verification to security protocol designers by introducing animation as a formal way to verify protocols and the soundness issue of animation. This paper proposes an innovative and iterative workflow to allow designers to carry our verification in the early stage of design with a guarantee that a problem detected during animation must be a bug in the design. Most importantly, animators are automatically generated from security protocol models. As a result, designers can find bugs earlier and deliver secure protocols efficiently without a loss of guarantee.

The integration of machine learning (ML) algorithms with edge sensor systems has fundamentally transformed numerous industries. This convergence empowers real-time data processing, analysis, and decision-making at the network’s periphery. This paper investigates the latest advancements in this domain by examining two key communities: Sensys-ML and TinyML. While Sensys-ML concentrates on optimizing ML for sensor systems, TinyML prioritizes deploying ML models on resource-constrained devices. Through a critical analysis of these communities’ contributions and interactions, this work aims to provide a comprehensive overview of cutting-edge methodologies, persistent challenges, and promising future directions for ML at the edge within sensor systems. By tracing the trajectory of advancements in this field, we offer a critical reflection on the broader research landscape and its scope. Additionally, we identify emerging research areas as reflected in prominent forums and underscore persisting knowledge gaps that call for further investigation.

In the rapidly expanding landscape of Internet of Things (IoT) device manufacturing and deployment, concerns about security have become prominent. This demonstration involves practical attacks on a thread-mesh network within a controlled environment, exploiting vulnerabilities in various components of the Thread network stack. Our attack vectors successfully identified nearby Thread networks and devices by gathering 2-byte Personal Area Network ID (PAN ID) and device frequency information, serving as reconnaissance for potential additional attacks. The focus was on investigating susceptibility to replay attacks and packet injection into thread-mesh networks. Although the experiment attempted to capture thread packets to emulate an authorised sender, the cryptographic encryption and sequence numbers employed for integrity checks resulted in packet rejection by the network. Despite this, our successful injection of packets highlights the potential for battery depletion attacks.

Lip-reading has become a critical research challenge, aiming to recognize speech by analysing lip movements. Traditional methods rely on cameras and wearable devices, but these face limitations like occlusion, lighting conditions, privacy concerns, and user discomfort. In the COVID-19 era, with face masks being the norm, such systems are ineffective for hearing aids. To overcome these issues, this paper introduces an RFID-based smart mask for lip-reading, enabling effective speech recognition even under face masks. The system collects data using RF sensing, capturing three classes: vowels (A, E, I, O, U), consonants (F, G, M, S), and words (Fish, Goat, Meal, Moon, Snake). Machine learning models classify the data, achieving high accuracy across individual classes. On the combined dataset, the Random Forest model achieves 80% accuracy, demonstrating the system’s potential to enhance accessibility and speech recognition for individuals with hearing impairments.

The paper titled “Terahertz Photoconductive Antenna Based on Tai-Chi Totem Structure for Cancer Detection” introduces an innovative design for a terahertz (THz) photoconductive antenna (PCA) utilising a Tai-Chi totem structure. This design aims to enhance the efficiency and sensitivity of THz radiation sources, which are crucial for non-invasive and accurate cancer detection. By leveraging the unique properties of the Tai-Chi totem configuration, the PCA achieves improved performance in generating and detecting THz waves, offering significant potential in medical diagnostics. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “A Fractal Dual-Band Polarization Diversity Antenna Array for 5G Communications” introduces a compact, dual-polarized antenna array tailored for the 28 GHz and 38 GHz frequency bands, which are pivotal for 5G applications. With dimensions of 32×20 mm², the antenna array achieves broad impedance bandwidths of 2.7 GHz (26.6–29.3 GHz) and 5.2 GHz (35–40.2 GHz). To facilitate polarization diversity, the design incorporates two orthogonal feed lines, enabling the antenna to support multiple polarization states. This innovation holds significant potential for enhancing the performance and versatility of 5G communication systems.

The paper titled “RIS-Enabled Near-Field Localisation with EMI” explores the utilisation of Reconfigurable Intelligent Surfaces (RIS) to enhance near-field localisation accuracy in environments with Electromagnetic Interference (EMI). By dynamically controlling electromagnetic signal characteristics such as scattering, reflection, and refraction, the study demonstrates how RIS technology can mitigate EMI effects, leading to improved precision in position estimation. This research holds significant potential for applications in wireless communication systems, indoor navigation, and IoT networks. The findings were presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “Ultrahigh Sensitive Terahertz Metasurface with 2D MoS₂ for Refractive Index Biosensing” introduces a highly sensitive terahertz (THz) metasurface integrated with two-dimensional molybdenum disulfide (2D MoS₂) for advanced refractive index biosensing applications. By leveraging the unique properties of 2D MoS₂, the metasurface achieves an enhanced quality (Q) factor, leading to improved sensitivity in detecting minute changes in the refractive index. This innovation holds significant potential for applications in biosensing, medical diagnostics, and environmental monitoring. The research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “Design of Intelligent Reflective Surface Unit Cell for 5G mmWave Communication Enhancement” presents a novel approach to improving 5G millimeter-wave (mmWave) communications through the development of an intelligent reflective surface (IRS) unit cell. The proposed design utilises a PIN diode switch to achieve a 180° phase shift in reflection mode between its ON and OFF states. The geometry of the unit cell’s top layer is optimised to support transverse electric (TE) polarized reflection with an angular stability of 30°, ensuring consistent performance across a range of incident angles. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “An Innovative Metasurface Polarizer Working in 5G Frequency Bands” introduces a novel metasurface design aimed at enhancing polarization control in 5G communications. The proposed metasurface comprises unit cells featuring a ring-shaped configuration with an internal star pattern, constructed on a Rogers 5880 substrate with a thickness of 1.575 mm and a loss tangent of 0.009. This design facilitates multi-band capabilities, supporting both cross-polarization and circular polarization, thereby improving signal quality and efficiency in 5G networks. The research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “RF-Based Respiration Disorders Sensing and Classification Using USRP” explores the application of Radio Frequency (RF) sensing technologies for real-time, contactless monitoring of respiratory disorders. Utilising Universal Software Radio Peripherals (USRP), the study demonstrates how RF signals can detect and classify various respiratory conditions, offering a non-invasive alternative to traditional monitoring methods. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN” explores the application of Deep Reinforcement Learning (DRL) to optimise resource allocation in Open Radio Access Networks (O-RAN). By jointly scheduling Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) services, the study aims to enhance network efficiency and meet diverse Quality of Service (QoS) requirements. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper “DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN” explores the application of Deep Reinforcement Learning (DRL) to tackle the challenge of resource scheduling in Open Radio Access Networks (O-RAN). By jointly optimising Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) traffic, the proposed DRL framework ensures efficient allocation of network resources while meeting the diverse Quality of Service (QoS) requirements of these services. This innovative approach paves the way for scalable and adaptive resource management in 5G and beyond, enhancing network performance and reliability. Presented at the 2024 IEEE International Conference on Communications Workshops, the research highlights significant advancements in AI-driven solutions for modern wireless networks.

Efficient utilisation of scarce resources, particularly radio spectrum and power, is a critical challenge in the development of future Internet of Things (IoT) networks. The paper “Throughput Optimisation in Ambient Backscatter-Based Cognitive IoT Networks” addresses this issue by proposing an innovative approach that combines ambient energy harvesting with backscatter communication in energy-constrained cognitive IoT networks. In this scheme, secondary network nodes effectively utilise the primary network’s resources. Depending on the availability of spectrum and energy, these nodes can switch between energy harvesting and backscatter communication modes, thereby optimising throughput and enhancing overall network efficiency.

In their study, “Performing QoS Measurements in UAV Field Trials with 5G-Advanced Communications at Various Altitudes,” presented at the IET 6G and Future Networks Conference (IET 6G 2024) in London, UK, the authors conduct field trials to assess Quality of Service (QoS) parameters of 5G networks in air mobility scenarios. They investigate latency, throughput, and handover attempts at altitudes of 50, 72, 88, and 110 meters, providing insights into the performance of UAV-to-ground connections across different heights. 

In “Moving Target Defence in 6G UAV Networks,” presented at the IEEE/AIAA 43rd Digital Avionics Systems Conference (DASC) in San Diego, Oct 2024, the authors explore innovative methods to enhance security in UAV networks using 6G technology. By leveraging predictive cyber-attack analysis, network monitoring, and management functions, the study demonstrates how to proactively secure communication data links. This work, recognized with the Best Session Award, highlights the potential of 6G-enabled computing to fortify UAV network defenses.  

Exploring the seamless integration of satellite and terrestrial networks, this publication addresses key challenges in 6G communications, including latency and dynamic user traffic. By leveraging a deep Q-learning-based algorithm for optimizing satellite trajectories, the research offers innovative solutions to enhance network reliability and performance. 

“This paper introduces a multi-band ultrathin reflective metasurface that supports polarization conversion across Ku, K, and Ka bands. The metasurface offers both linear and circular polarization options, making it a versatile solution for advanced communication and sensing applications.”

“This study delves into the use of reconfigurable intelligent surfaces (RIS) for precise indoor localization in ‘coded environments.’ By leveraging data-driven approaches, this research enhances indoor navigation, with potential applications in smart buildings and automated environments.”

“This paper presents the concept of ‘intelligent wireless walls,’ designed for contactless in-home monitoring. The technology enables continuous health and safety monitoring by transforming walls into sensor networks, offering potential applications in elder care and home security through seamless, unobtrusive monitoring.”

Papers before 2023 were not funded by CHEDDAR but are here as they influenced the design of the hub.

“In this innovative study, the authors explore the use of remote RF sensing to achieve lip reading even when individuals are wearing face masks. This research pushes the boundaries of RF technology, presenting methods that could be transformative for communication in healthcare and other masked environments.”

Papers before 2023 were not funded by CHEDDAR but are here as they influenced the design of the hub.

“This paper introduces a comprehensive multimodal dataset aimed at advancing contactless lip reading and acoustic analysis. By combining visual and audio modalities, the dataset provides valuable resources for developing robust algorithms that can accurately interpret speech in challenging environments, enhancing applications in fields like accessibility and remote communication.”

“In this paper, the authors explore data-driven approaches to indoor localization using reconfigurable intelligent surfaces (RIS). This ‘coded environment’ approach enhances accuracy in indoor navigation, making it a promising solution for applications such as smart buildings and automated logistics.”

“This study investigates the use of reconfigurable intelligent surfaces (RIS) to improve near-field localization accuracy. By implementing a practical phase shift model, the research demonstrates how RIS technology can enable precise localization, with potential applications in indoor navigation and IoT.”

“This research explores the development of a 60 GHz programmable dynamic metasurface antenna (DMA), showcasing its potential for next-generation communication and imaging systems. The paper provides a detailed journey from concept to prototype, emphasizing DMA’s role in enhancing wireless communication and imaging precision.”

“This paper introduces a wideband series-fed patch antenna array optimized for 5G Vehicle-to-Everything (V2X) communications. The array’s high gain and low sidelobe characteristics, coupled with its linearly and circularly polarized configurations, make it ideal for improving connectivity and reliability in 5G-enabled transportation systems.”

“This study presents a novel sub-terahertz (THz) planar antenna array designed to enhance sensing and imaging applications. With a focus on achieving high performance in THz frequencies, the research highlights significant advancements in antenna design, providing applications in fields such as medical imaging and environmental monitoring.”