Federated learning (FL) and reconfigurable intelligent surfaces (RIS) are pivotal technologies for future Internet of Things (IoT) networks, enhancing user privacy and system efficiency. However, realizing their full potential necessitates a cohesive and synergistic integration, challenging the traditional view of them as disparate components. This paper tackles the complex problem of maximizing energy efficiency (EE)—a critical yet under-explored metric insuch tightly coupled FL-RIS systems. We address this gap by formulating ajoint optimization problem that intrinsically links the FL process with physical layer resource allocation. Our framework maximizes the system’s global EE by concurrently designing the base station’s active beamforming and the RIS’s passive phase shifts,with an FL aggregation mechanism that is explicitly channel-aware and adaptive to the RIS-optimized wireless environment. This co-design ensures RIS actively facilitates FL by establishing robust communication, while FL intelligently leverages these improved channels for efficient and accelerated learning, all under practical FL performance constraints. Simulation results demonstrate that our proposed framework significantly enhances system energy efficiency compared to several benchmark schemes and exhibits robust convergence properties.

Traditional covert ambient backscatter communication (AmBC) systems suffer from a fundamental throughput limitation governed by the square root law (SRL), restricting reliable covert transmission toO(√n) bits overnchannel uses. To overcome this limitation, we introduce a non-colluding replay node that retransmits ambient radio frequency (RF) signals with randomized power, significantly increasing channel uncertainty faced by an adversarial warden (Willie) while preserving compatibility with low-power AmBC architectures. Through rigorous theoretical analysis, we demonstrate that this approach enables linear scaling of covert throughput without necessitating power reduction or prior knowledge of ambient RF signal characteristics. Furthermore, it guarantees that Willie’s total detection error probability can be driven arbitrarily close to 1, specificallyPFA+PMD= 1 − ϵ for any ϵ > 0, simultaneously achieving an arbitrarily low decoding error probability at the legitimate receiver (Bob). Unlike conventional jamming-based solutions requiring stringent synchronization or complex multi-antenna configurations, our replay mechanism operates independently from covert communication participants, substantially simplifying the decoding architecture for the legitimate receiver and reducing synchronization overhead. By increasing the ambient signal power uncertainty, the proposed architecture provides a robust, scalable framework suitable for high-rate covert communication scenarios in IoT and privacy-sensitive applications, achieving an effective balance among covertness, energy efficiency, and system robustness.

Semantic communication (SemCom) has recently emerged as a promising paradigm for enhancing the efficiency and intelligence of wireless networks. Nevertheless, device het erogeneity, resource constraints, and the vulnerability of deep neural networks in open environments pose significant challenges to its practical deployment. In this paper, we propose a task oriented and lightweight SemCom system with secure aggregation for ensuring efficient and privacy-preserving interactions in distributed networks. First, we design a multi-task SemCom framework that unifies semantic feature extraction from sample based datasets. To accommodate resource-constrained devices, we further introduce a feature distillation mechanism that derives lightweight local models without sacrificing inference accuracy. To preserve the privacy of local datasets while leveraging the generalization capability of distributed devices, we develop a secure model aggregation algorithm based on multiparty homomorphic encryption. Simulation results and comparative experiments validate the effectiveness of our system, which fully utilizes the knowledge embedded in existing high-performance models. Our results demonstrate that the proposed local semantic models outperform the baseline models under limited datasets and reduced parameters. We also analyze the trade-off between computational complexity and security in the proposed aggregation scheme, highlighting its applicability to distributed SemCom scenarios.

Semantic communications are expected to improve the transmission efficiency in Internet of Things (IoT) networks. However, the distributed nature of networks and heterogeneity of devices challenge the secure utilization of semantic communication systems. In this paper, we develop a distributed semantic communication system that achieves the security and efficiency during update and usage phases. A blockchain-based trust scheme for update is designed to continuously train and synchronize the system in dynamic IoT environments. To improve the updating efficiency, we propose a flexible semantic coding method base on compressive semantic knowledge bases. It greatly reduces the amount of data shared among devices for system update, and realizes the flexible adjustment of the size of knowledge bases and the number of transmitted signal symbols in model training and inference stages. In the usage phase, a signature mechanism for lossy semantics is introduced to guarantee the integrity and authenticity of the transmitted semantics in lossy semantic communications. We further design a noise-aware differential privacy mechanism, which introduces optimized noise based on the different channel information available to heterogeneous devices. Experiments on transmission tasks show that the proposed system defends against cross-phase attacks of compromising semantics integrity and reduces the data to be shared in the update phase by about 36% to 90%, and in the usage phase by 60% compared with related works.

Specific Emitter Identification (SEI) is pivotal for ensuring the security of the Internet of Things (IoT). Traditional deep learning-based SEI techniques often falter in real-world applications, particularly when distinguishing between legitimate and rogue devices amid noisy conditions and low Signal-to-Noise Ratios (SNR). To surmount these challenges, we propose a novel open-set SEI (OS-SEI) strategy that utilizes a Metric-enhanced Denoising Auto-encoder (MeDAE) architecture. This advanced framework incorporates a deep residual shrinkage network, significantly augmenting the denoising autoencoder’s capability, thereby bolstering its resilience against noisy environments. Further, the integration of discriminative metrics, such as center loss, markedly enhances feature discrimination, resulting in heightened accuracy of device identification. Our comprehensive experimental assessments, conducted on an Automatic Dependent Surveillance-Broadcast (ADS-B) dataset, underscore the superiority of our proposed OS-SEI method over existing models. The findings confirm our approach’s enhanced robustness to noise and its superior accuracy in device identification within open-set scenarios.

Specific emitter identification (SEI) is a passive physical layer authentication technology that mines subtle hardware differences between emitters to identify devices. However, traditional deep learning-based SEI is trained for scenarios with massive signal samples and performs poorly in sample-limited scenarios. To solve this problem, we proposed a robust few-shot SEI (FS-SEI) method using dual-level data augmentation, consisting of phase shift position prediction and manifold cutMix (P3MC). We perform data augmentation in both the sample space and the feature space to accelerate the complex valued time series lightweight adaptive network (CV-TSLANet) to learn robust features and use machine learning to identify ADS-B emitters. Our experimental results show that the performance of our proposed FS-SEI method reaches 90% when the number of samples per category is 30. We have open-sourced the proposed FS-SEI method at https://github.com/IcedWatermelonJuice/P3MC.

In this paper, we introduce an innovative Radio Frequency Fingerprinting (RFF)-based device authentication scheme for the Internet of Things (IoT), a network marked by extensive interconnections and interactions among various entities. Our approach, designed for an open and dynamic communication environment, not only identifies devices encountered during training but also effectively rejects those not previously seen. The scheme employs a hypersphere projection for feature embedding, strategically avoiding the need to optimize intra-device variations in the radial direction. It uses a K-Means-based binary classifier for initial device assessment based on cosine similarity scores, followed by a SoftMax classifier for precise identification of known devices. Our extensive numerical analysis confirms that this method delivers superior performance, setting a new benchmark in RFF authentication for IoT security.

In the context of emerging 6G technology challenges, this paper introduces the LSMFF-AMC approach, leveraging multimodal feature fusion (MFF) with Long-Short range attention (LSRA) to enhance automatic modulation classification(AMC). The method significantly boosts classification accuracy by employing convolutional neural networks (CNN) for diverse modal feature extraction and integrating LSRA for comprehensive feature combination. Our experiments demonstrate an increase in accuracy from 88% to nearly 97%, outperforming traditional single-modal approaches. Additionally, a convergence analysis of the training loss function reveals LSMFF-AMC's superior and faster convergence compared to standard AMC methods.

Radar emitter identification (REI) is a crucial function of electronic radar warfare support systems. The challenge emphasizes identifying and locating unique transmitters, avoiding potential threats, and preparing countermeasures. Due to the remarkable effectiveness of deep learning (DL) in uncovering latent features within data and performing classifications, deep neural networks (DNNs) have seen widespread application in REI. In many real-world scenarios, obtaining a large number of annotated radar transmitter samples for training identification models is essential yet challenging. Given the issues of insufficient labeled datasets and abundant unlabeled training datasets, we propose a novel REI method based on a semi-supervised learning (SSL) framework with virtual adversarial training (VAT). Specifically, two objective functions are designed to extract the semantic features of radar signals: computing cross-entropy loss for labeled samples and virtual adversarial training loss for all samples. Additionally, a pseudo-labeling approach is employed for unlabeled samples. The proposed VAT-based SS-REI (SS-VAT) method is evaluated on a radar dataset. Simulation results indicate that the proposed SS-VAT method outperforms the latest SS-REI method in recognition performance.

Deep learning (DL), renowned for its superior feature extraction capabilities, has remarkably succeeded in specific emitter identification (SEI), especially when supported by high-quality labeled data. However, obtaining accurate signal labels in complex electromagnetic environments is challenging, and manual labeling is prone to errors, underscoring the need for robust DL-based SEI methods that can handle label noise. These methods prevent neural networks from overfitting noisy labels, thereby boosting identification performance. Yet, research in this area is still limited. Our study introduces a robust label-noise SEI approach and the sample selection and regularization (SSR) method. This involves a two-stage adaptive sample selection (ASS) driven by confidence learning. The first stage entails coarse-grained separation of true and false labels through direct deep neural network (DNN) training. In the second stage, semi-supervised learning (SSL) utilizes a regularization-inspired loss, incorporating label smoothing regularization (LSR) and entropy minimization (EM), for fine-grained sample selection. The DNN is ultimately trained on precisely selected true-labeled samples. Comparative experiments on the automatic dependent surveillance-broadcast (ADS-B) and Wi-Fi data sets demonstrate that our SSR method outperforms the existing methods in identification accuracy, particularly at a 20% label-noise ratio, achieving 86.00% accuracy with the ADS-B data set, and 99.38% with the Wi-Fi data set. The code is available at:https://github.com/sleepeach/SSR-SEI.

Ensuring network security, effective malware detection is of paramount importance. Traditional methods often struggle to accurately learn and process the characteristics of network traffic data, and must balance rapid processing with retaining memory for previously encountered malware categories as new ones emerge. To tackle these challenges, we propose a cutting-edge approach using self-paced class incremental learning (SPCIL). This method harnesses network traffic data for enhanced class incremental learning (CIL). A pivotal technique in deep learning, CIL facilitates the integration of new malware classes while preserving recognition of prior categories. The unique loss function in our SPCIL-driven malware detection combines sparse pairwise loss with sparse loss, striking an optimal balance between model simplicity and accuracy. Experimental results reveal that SPCIL proficiently identifies both existing and emerging malware classes, adeptly addressing catastrophic forgetting. In comparison to other incremental learning approaches, SPCIL stands out in performance and efficiency. It operates with a minimal model parameter count (8.35 million) and in increments of 2, 4, and 5, achieves impressive accuracy rates of 89.61%, 94.74%, and 97.21% respectively, underscoring its effectiveness and operational efficiency.

Specific emitter identification (SEI) has emerged as a notable device authentication technology, distinguishing various emitters through the unique radio frequency fingerprint (RFF) inherent in wireless devices. Traditional SEI methods, often hindered by time-consuming manual feature extraction, struggle with complex encrypted signals. The advent of deep learning, with its robust feature extraction capabilities, has significantly advanced SEI, yet it typically demands extensive radio frequency signal samples and falters with limited (i.e., few-shot) samples. Our proposed few-shot SEI (FS-SEI) approach, integrating neural architecture search (NAS) and advanced deep transfer learning (DTL), adeptly identifies few-shot long-range (LoRa) devices. This method begins with NAS to autonomously tailor optimal network architectures for SEI tasks, followed by pre-training on extensive auxiliary datasets to extract general RFF features of LoRa devices. Transfer learning then fine-tunes these features for distinctiveness with compact intra-class distances. By only utilizing few-shot LoRa data for final parameter adjustments, the classifier rapidly assimilates new categories. Simulations confirm our FS-SEI method’s superior accuracy over classical approaches, with visualized feature analysis underscoring its distinguishing and generalizing prowess.

Automatic dependent surveillance-broadcast (ADS-B) has been widely deployed on aircraft to facilitate aviation information exchange and improve air traffic safety. However, its broadcast nature and lack of security considerations like encryption and authentication have caused the counterfeit of ADS-B messages to be straightforward. Flooding forged messages to legitimate aircraft, denial-of-service (DoS) attacks threaten flight security severely. In this paper, we propose a practical security solution against DoS attacks on ADS-B based on high-precision timestamp and position information. The solution achieves high feasibility and reliability by accommodating measurement errors of physical quantities. Besides, it preserves ADS-B frame size and ensures efficient computation in frame generation and verification. Comprehensive security analyses demonstrate robust filtrations of the proposed solution on malicious messages from DoS adversaries with different capabilities. Further simulations on real-world aviation data exhibit significant defensive performance realized by the DoS-immune ADS-B security solution. Whether adversaries can only intercept ciphertext, or they have opportunities to acquire decrypted messages, all DoS attacks on ADS-B are successfully thwarted by the solution. Even for adversaries with victim aircraft location estimation capacity, the solution resists all DoS attacks transmitting less than 50 forged messages per second.

In cognitive radio networks, cooperative spectrum sensing (CSS) is a key approach to effectively discover spectrum opportunities for secondary users. However, due to the presence of malicious nodes, CSS faces significant challenges in the trust issue of sensing results caused by spectrum sensing data falsification and the privacy leakage of sensing nodes. In this article, we develop a trustworthy and privacy-preserving CSS solution based on blockchain, TaP2-CSS. It achieves the transparency and trustworthiness in exchanging and fusing sensing reports and preserves privacy of sensing nodes. More specifically, a fusion scheme is proposed to realize the high defense capability against the spectrum sensing falsification attack launched by lurking and persistent malicious nodes. Furthermore, to address privacy threats of sensing nodes, we propose a privacy-preserving sensing scheme based on dynamic sensing time for resource-constrained sensing nodes. It effectively limits the location information leaked by sensing reports without the need for complex cryptographic computation and protocol interaction. Comprehensive evaluation and comparison show that the proposed solution achieves high sensing accuracy in the presence of malicious nodes while preserving the privacy of sensing nodes.

Recently, an emerging branch of research in the field of fully homomorphic encryption (FHE) attracts growing attention, where optimizations are carried out in developing fast and efficient homomorphic logic circuits. While existing works have pointed out that compound homomorphic gates can be constructed without incurring significant computational overheads, the exact theory and mechanism of homomorphic gate design have not yet been explored. In this work, we propose AutoHoG, an automated procedure for the generation of compound gates over FHE. We show that by formalizing the gate generation procedure, we can adopt a match-and-replace strategy to significantly improve the evaluation speed of logic circuits over FHE. In the experiment, we first show the effectiveness of AutoHoG through a set of benchmark gates. We then apply AutoHoG to optimize common Boolean tasks, including adders, multipliers, the ISCAS’85 benchmark circuits and the ISCAS’89 benchmark circuits. We show that for various circuit benchmarks, we can achieve up to 5.7× reduction in computational latency when compared to the state-of-the-art implementations of logic circuits using conventional gates.

Specific emitter identification (SEI) plays a crucial role in the security of the Industrial Internet of Things (IIoT). In recent years, research on applying deep learning (DL) methods for signal identification has mushroomed. However, DL-based SEI methods rely on a huge amount of training data and powerful computing devices, limiting their application scenarios. In addition, DL models are considered black box models with poor interpretability. To solve the above problems, this paper proposes a novel few-shot SEI solution using knowledge graph-driven incremental broad learning (KG-IBL). Specifically, this paper uses a deep belief network (DBN) to dig deep into features and expand the broad structure with additional enhancement nodes. Furthermore, the proposed KG-IBL does not need to retrain all data to achieve dynamic incremental update learning. To our knowledge, this is the first endeavor to integrate KG with broad learning for addressing the few-shot SEI problem. The experimental results demonstrate that the proposed KG-IBL surpasses existing incremental methods in both identification performance and computational overhead. Last but not least, the accuracy of the proposed KG-IBL is 97.5%, which is only 1.67% lower than the theoretical upper limit, and the training time is nearly 267 times lower than that of deep learning models. The code and dataset are available for download athttps://github.com/Lollipophua/KG-IBL.

The evolution of wireless communication technologies has brought significant conveniences but also raised security concerns. Radio frequency fingerprint (RFF) is a potential feature, which can uniquely identify a specific emitter. The integration of Deep Learning (DL) has further enhanced the reliability of RFF identification. However, DL methods often struggle in dynamic communication environments. In this paper, we propose a dynamic adaptive RFF identification method leveraging Cognitive Representation Learning (CRL). Our proposed method is capable of recognizing and storing cognitive knowledge from historical environments. Furthermore, it dynamically adapts to current situations through its cognitive module, offering enhanced adaptability in dynamic environments. Specifically, we analyze the causes of RFF and define the RFF identification problems at first. Secondly, our cognitive module evaluates current data by examining both data distribution and feature distribution distances. Concurrently, our representation learning strategy enhances feature reuse and focuses on feature space. Finally, we implement an unsupervised ensemble module, combining unsupervised clustering with model ensemble techniques to boost performance. Simulation results validate our method’s robust generalization in dynamic settings, with an improvement of 7.66% in controlled environments and 5.98% in more challenging scenarios on PA dataset. Furthermore, the high identification ratio and ablation study results underscore the efficacy and necessity of each module in our approach.

Cyber security has always been an important issue in the Internet of Everything topic. In the physical layer of the Internet, specific emitter identification (SEI) technology is widely researched as a simple and effective intrusion prevention technology. Existing SEI research only focused on radio frequency (RF) signals from a single receiver. However, in real scenes such as the Industrial Internet of Things (IIoT), vehicle-to-everything applications, and intelligent sensing systems, etc., RF signals are received from different types of sensors deployed at different locations. Therefore, this paper proposes a multisource heterogeneous SEI (MH-SEI) method and proposes a multi-source heterogeneous attention-based feature fusion network (MHAFFN) to achieve excellent identification performance. The proposed MHAFFN utilizes a multi-channel convolutional network as the RF fingerprinting (RFF) extraction module for multisource heterogeneous RF signals and equips an attention-based RFF fusion module to obtain mixed RFF for the automatic classifier. The experimental results show that the identification accuracy of MHAFFN is 99.196% in a perfect environment. Furthermore, robustness verification has proved that MHAFFN keeps advantages in noisy environments. Through fault tolerance mechanism verification experiment, it is proved that MHAFFN is able to work stably in real-world complex scenarios.

With the emergence of fifth-generation (5G) technology, the air-to-ground (ATG) communication system based on 5G public mobile communication technology makes it possible to provide data services for in-flight communication. Customer premises equipment (CPE) is connected to ATG base stations to provide network access for user devices in-cabin. However, the handover authentication performed by CPEs during a handover between ATG base stations poses security risks and incurs a lot of overhead. Furthermore, frequent handovers between base stations and the limited computational resources of the CPE make the protection of security properties even more challenging. In this paper, we propose a lightweight group pre-handover authentication scheme for aviation 5G ATG networks. By leveraging the predictability of flight paths, all CPEs on the same aircraft can complete handover authentication before arriving at the next base station, and handover authentication delay can be ignored, providing seamless communication services for user devices in the cabin. The scheme utilizes the extended Chebyshev chaotic map and symmetric encryption technology to reduce computational overhead and adapt to the limited computing resources of CPE. Security and performance analysis show that our scheme achieves lightweight and efficiency while ensuring security performance and outperforms other relevant schemes.

Aircraft Communications Addressing and Reporting System (ACARS) has been widely used in aviation datalink. However, for lack of security designs, ACARS faces increasing security threats such as eavesdropping and message injection. Although several security solutions has been proposed on aviation surveillance message, such as Automatic Dependent Surveillance-Broadcast, those on ACARS have received far less attention. To further improve the session security and privacy of civil aviation users, we put forwards a compatible protocol for ACARS datalink to protect message security as well as aircraft identity privacy. The proposed solution provides communication confidentiality, and supports data integrity and user identity verification. Meanwhile, by replacing the aircraft’s identity transmitted in plaintext with a variable anonymity, the privacy of an aircraft is protected from the disclosure of aircraft identity. Moreover, our protocol is compatible with current ACARS standards, making the proposed solution easy-to-deploy and practical. Formal analysis and simulations are carried out to make sure the security of proposed protocol.

Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning becomes popular in finding various neural architectures that fit diverse Artificial Intelligence of Things (AIoT) scenarios. Recently, to prevent the leakage of private information while enable automated machine intelligence, there is an emerging trend to integrate federated learning and neural architecture search (NAS). Although promising as it may seem, the coupling of difficulties from both tenets makes the algorithm development quite challenging. In particular, how to efficiently search the optimal neural architecture directly from massive nonindependent and identically distributed (non-IID) data among AIoT devices in a federated manner is a hard nut to crack. In this article, to tackle this challenge, by leveraging the advances in ProxylessNAS, we propose a federated direct neural architecture search (FDNAS) framework that allows for hardware-friendly NAS from non-IID data across devices. To further adapt to both various data distributions and different type of devices with heterogeneous embedded hardware platforms, inspired by meta-learning, a cluster federated direct neural architecture search (CFDNAS) framework is proposed to achieve device-aware NAS, in the sense that each device can learn a tailored deep learning model for its particular data distribution and hardware constraint. Extensive experiments on non-IID data sets have shown the state-of-the-art accuracy–efficiency tradeoffs achieved by the proposed solution in the presence of both data and device heterogeneity.

This paper presents an initial effort for the trusted geolocation of Internet of Things (IoT) devices based on the booming low-earth-orbit (LEO) satellites. As the high signal-to-noise ratio (SNR) reception cannot always be guaranteed at LEO satellites, the recently developed direct position determination (DPD) approach is adopted. For such passive localization systems, the efficient DPD execution is challenging due to the vast coverage area of LEO satellites. In order to reduce the computational complexity, we propose a method to narrow the search area using the time difference of arrival (TDOA) measurements and error variances. In this way, the size of the searching area is determined by both geometrical constraints and qualities of received signals, and signals with a higher SNR are more effective in positioning as their search areas are usually smaller. The superior accuracy performance of the proposed method is also verified through the comparison with conventional two-step methods. Mento Carlo simulations show that the proposed approach provides a robust and trusted localization service, and the positioning error is less than 10 kilometers when the SNR is lower than −15dB.

While deploying on edge devices, deep learning mod-els often encounter various strict resource constraints. Automated machine learning becomes popular in finding various neural architectures that fit diverse Internet of Things (IoT) scenarios to handle these problems with less human efforts. Recently, there is an emerging trend to integrate federated learning and Neural Architecture Search (NAS) to prevent private data leakage while enabling automated machine learning. The algorithm development is quite challenging because of the coupling of difficulties from both tenets, although promising as it may seem. Especially, it is a hard nut to efficiently search the optimal neural architecture directly from massive non-Independent and Identically Distributed (non-IID) data among IoT devices in a federated manner. In this paper, by leveraging the advances in ProxylessNAS, we propose a Federated Direct Neural Architecture Search (FDNAS) framework that allows hardware-friendly NAS from non-IID data across devices to tackle the challenge. Extensive experiments on non-IID datasets demonstrate the state-of-the-art accuracy-efficiency trade-offs achieved by proposed methods.

Nowadays, people usually connect to the Internet through a multitude of different devices. Video streaming takes the lion's share of the bandwidth, and represents the real challenge for the service providers and for the research community. At the same time, most of the connections come from indoor, where Wi-Fi already experiences congestion and coverage holes, directly translating into a poor experience for the user. A possible relief comes from the TV white space (TVWS) networks, which can enhance the communication range thanks to sub-GHz frequencies and favorable propagation characteristics, but offer slower datarates compared with other 802.11 protocols. In this paper, we show the benefits that TVWS networks can bring to the end user, and we present CABA, a connection aware balancing algorithm able to exploit multiple radio connections in the favor of a better user experience. Our experimental results indicate that the TVWS network can effectively provide a wider communication range, but a load balancing middleware between the available connections on the device must be used to achieve better performance. We conclude this paper by presenting real data coming from field trials in which we streamed an MPEG dynamic adaptive streaming over HTTP video over TVWS and Wi-Fi. Practical quantitative results on the achievable quality of experience for the end user are then reported. Our results show that balancing the load between Wi-Fi and TVWS can provide a higher playback quality (up to 15% of average quality index) in scenarios in which the Wi-Fi is received at a low strength.

Operating at ultra-high frequency (UHF), TV white space (TVWS) can achieve long-distance communication and good in-building penetration, and has attracted increasing attention of regulators, researchers and stakeholders. This paper explores the potential of TVWS for network provisioning within a cluster of buildings, through a succession of tests. Different transmission distances, from 10m to over 120m, and through multiple layers of walls as well as complex transmission environment imposed by other factors like office and construction facilities, are considered. Further, a compact ultra-wide band (UWB) printed monopole antenna is designed for the client white space terminal, and compared with a commercial directional UHF antenna on the same client. Measurement results show that the in-house compact antenna achieves fast network speed and a high signal-to-interference-plus-noise ratio (SINR), and it is orientation independent.

TV White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large- scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper provides an update on the trials, giving an overview of their objectives and characteristics, some aspects that have been covered, and some early results and observations.