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The pervasive nature of wireless telecommunication has made it the foundation for mainstream technologies like automation, smart vehicles, virtual reality, and unmanned aerial vehicles. As these technologies experience widespread adoption in our daily lives, ensuring the reliable performance of cellular networks in mobile scenarios has become a paramount challenge. Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users' location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages and implications. Notably, the study presents an impressive 53% improvement in signal-to-noise ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and Zero forcing (ZF) techniques, alongside deep neural networks (DNN) employing Bayesian Optimization (BO), represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino, in enabling location-aware beamforming.

Proactive content caching has emerged as a promising solution to cope with exponentially increasing mobile data traffic. The popular user contents can be cached near the network edge for faster retrieval and processing. Current state-of-the-art approaches adopt a centralized model training mechanism that requires high communication and data exchange overheads to predict content popularity. Moreover, these approaches fail to deal with the dynamicity of the environment since they do not take into account the users’ mobility information and are unable to incorporate content offload timings. In this paper, we address these limitations by proposing a novel federated learning-based Mobility and Demand-aware Proactive Content Offloading (MDPCO) framework. MDPCO exploits distributed learning strategies and capitalizes on users’ mobility and demand information for proactive content offloading. Extensive simulations are carried out to validate the efficacy of MDCPO against local and cloud-based models. Our proposed model yields an average performance improvement of 6.7% in comparison to the cloud-based model. Furthermore, with the increase in the number of fog servers, the MDPCO achieves a 9.8% higher data offloading ratio and 1.18% increase in the downlink rates while being more energy-efficient than cloud-based approaches.

For vehicle-to-network communications, handover (HO) management enables vehicles to maintain the connection with the network while transiting through coverage areas of different base stations (BSs). However, the high mobility of vehicles means shorter connection periods with each BS that leads to frequent HOs, hence raises the necessity for optimal HO decision making for high quality infotainment services. Machine learning is capable of capturing underlying pattern via data driven methods to find optimal solutions to complex problems, and much learning-based HO optimization research has been conducted focusing on specific network setups. However, attention still needs to be paid to the actual deployment aspect and standardized datasets or simulation environments for evaluation. This paper proposes a deep reinforcement learning-based HO algorithm using the input parameters that are configurable in the existing measurement report of cellular networks. The performance of the proposed algorithm is evaluated using the well-known network simulator ns-3 with its official LTE module. A realistic network setup in the city center of Glasgow (UK) is configured with vehicle trajectories generated by the routes mobility model using Google Maps Directions API. Evaluation results reveal that the proposed algorithm significantly outperforms the A3 RSRP baseline with an average of 25.72\% packet loss reduction per HO, suggesting significant improvement in quality of service of phone call and video streaming, etc. The proposed algorithm also has a small implementation cost compared to some state-of-the-art and should be deployed by a software update to a local BS controller.

This paper presents a framework for achieving machine learning (ML)‐assisted direction‐of‐arrival (DoA) accuracy enhancement using a millimetre‐wave (mmWave) dynamic aperture. The technique used for the enhanced DoA estimation accuracy leverages an over‐sized lens‐loaded cavity antenna connected to a single RF chain in the physical layer and a computational method in the computational layer of the framework. It is shown for the first time that by introducing a reconfigurable mode‐mixing mechanism inside the over‐sized lens‐loaded cavity hardware, a greater number of spatially orthogonal radiation modes can be achieved giving rise to many cavity states. If the best cavity state is determined and selected by means of design exploration using a contemporary ML‐assisted antenna optimisation method, the computational DoA estimation accuracy can be improved. The mode‐mixing mechanism in this work is a randomly oriented metallic scatterer located inside an over‐sized constant−ϵ r lens‐loaded cavity, connected to a stepper motor that is electronically controlled by inputs from the computational layer of the presented framework. Measurement results in terms of near‐field radiation mode scans are included in this study to verify and validate that the proposed ML‐assisted framework enhances the DoA estimation accuracy. Moreover, this investigation simultaneously provides a simplification in the physical layer implementation of mmWave radio hardware, and DoA accuracy enhancement, which in turn lends itself favourably to the adoption of the proposed framework for channel sounding in mmWave communication systems.

Life on earth depends on healthy oceans, which supply a large percentage of the planet's oxygen, food, and energy. However, the oceans are under threat from climate change, which is devastating the marine ecosystem and the economic and social systems that depend on it. The Internet-of-underwater-things (IoUTs), a global interconnection of underwater objects, enables round-the-clock monitoring of the oceans. It provides high-resolution data for training machine learning (ML) algorithms for rapidly evaluating potential climate change solutions and speeding up decision-making. The sensors in conventional IoUTs are battery-powered, which limits their lifetime, and constitutes environmental hazards when they die. In this paper, we propose a sustainable scheme to improve the throughput and lifetime of underwater networks, enabling them to potentially operate indefinitely. The scheme is based on simultaneous wireless information and power transfer (SWIPT) from an autonomous underwater vehicle (AUV) used for data collection. We model the problem of jointly maximising throughput and harvested power as a Markov Decision Process (MDP), and develop a model-free reinforcement learning (RL) algorithm as a solution. The model's reward function incentivises the AUV to find optimal trajectories that maximise throughput and power transfer to the underwater nodes while minimising energy consumption. To the best of our knowledge, this is the first attempt at using RL to ensure sustainable underwater networks via SWIPT. The scheme is implemented in an open 3D RL environment specifically developed in MATLAB for this study. The performance results show up 207% improvement in energy efficiency compared to those of a random trajectory scheme used as a baseline model.

Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research. 2023, The Author(s). This work is supported in parts by EPSRC grant no: EP/T021063/1 and Ajman University Internal Research Grants No. [2021-IRGDEN-7 and RTG-2022-DEN-01]. The research findings presented in this article are solely the author(s) responsibility. Scopus

While the fifth generation (5G) is bringing an innovative fabric of breakthrough technologies, enabling smart factories, cities, and Internet-of-Things (IoT), the unprecedented strain on communication networks put by these applications, in terms of highly cognitive, agile architectures and the support of massive connectivity, energy efficiency, and extreme ultralow latency, is pushing 5G to their limits. As such, the focus of academic and industrial efforts has shifted toward beyond 5G (B5G) and the conceptualization of sixth generation (6G) systems. This article discusses four main digital and societal use cases (UCs) that will drive the need to reconcile a new breed of network requirements. Based on this, we provide our vision of the fundamental architectural ingredients that will enable the promise of 6G networks of bringing the unification of experiences across the digital, physical, and human worlds. We outline key disruptive technological paradigms that will support 6G materialize a bouquet of unique expectations and redefine how we live and protect our planet. Finally, we adopt the recently envisaged ecosystem of the Internet-of-Musical Things (IoMusT) to depict how the discussed UCs and technological paradigms may be exploited to realize this ecosystem.

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution (S$^{\text{4}}$) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of S$^{\text{4}}$ in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks., Comment: This paper has been submitted to IEEE Transactions on Wireless Communications for the second round of peer review after a major revision

Intelligent transportation systems increasingly depend on wireless communication, facilitating real-time vehicular communication. In this context, message authentication is crucial for establishing secure and reliable communication. However, security solutions must consider the dynamic nature of vehicular communication links, which fluctuate between line-of-sight (LoS) and non-line-of-sight (NLoS). In this paper, we propose a lightweight cross-layer authentication scheme that employs public-key infrastructure-based authentication for initial legitimacy detection while using keyed-based physical-layer re-authentication for message verification. However, the latter's detection probability (P_d) decreases with the reduction of the signal-to-noise ratio (SNR). Therefore, we examine using Reconfigurable Intelligent Surface (RIS) to enhance the SNR value directed toward the designated vehicle and consequently improve the P_d, especially for NLoS scenarios. We conducted theoretical analysis and practical implementation of the proposed scheme using a 1-bit RIS, consisting of 64 x 64 reflective units. Experimental results show a significant improvement in the P_d, increasing from 0.82 to 0.96 at SNR = - 6 dB for an orthogonal frequency division multiplexing system with 128 subcarriers. We also conducted informal and formal security analyses, using Burrows-Abadi-Needham (BAN)-logic, to prove the scheme's ability to resist passive and active attacks. Finally, the computation and communication comparisons demonstrate the superior performance of the proposed scheme compared to traditional crypto-based methods., Comment: 12 pages, 12 figures and 5 tables

This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided Dynamic blockage Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model capable of simultaneously predicting blockage status and time. This enables determining the optimal point for proactive handover (PHO) or beam switching, thereby reducing the latency introduced by 5G new radio procedures and ensuring high quality of experience (QoE). The framework employs radar sensors to monitor and track objects movement, generating range-angle and range-velocity maps that are useful for scene analysis and predictions. Moreover, FL provides additional benefits such as privacy protection, scalability, and knowledge sharing. The framework is assessed using an extensive real-world dataset comprising mmWave channel information and radar data. The evaluation results show that RaDaR substantially enhances network reliability, achieving an average success rate of 94% for PHO compared to existing reactive HO procedures that lack proactive blockage prediction. Additionally, RaDaR maintains a superior QoE by ensuring sustained high throughput levels and minimising PHO latency.

In this paper, a compact, efficient and easy to fabricate wearable antenna integrated with Artificial Magnetic Conductor (AMC) is presented. Addition of slots and bevels/cuts in the rectangular monopole patch antenna yield a wide bandwidth along with band notches. The proposed antenna is backed with an AMC metasurface that changes the bidirectional radiation pattern to a unidirectional, thus, considerably reducing the Specific Absorption Ratio (SAR). The demonstrated antenna has a good coverage radiating away from the body and presents reduced radiation towards the body with a front-to-back ratio of 13 dB and maximum gain of 3.54 dB. The proposed design operates over a wide frequency band of 2.9 to 12 GHz (exceeding the designated 3.1−10.6 GHz Ultra-Wideband (UWB) band). The band notches were created using slots on the radiating patch in the sub-bands from 5.50 to 5.67 GHz and 7.16 to 7.74 GHz. The overall dimensions of the structure are 33 × 33 × 6.75 mm3. The antenna's radiation performance increased considerably with the addition of the AMC layer. The SAR values for the antenna are reduced by 85.3% when the AMC is used and are 0.083 W/kg which is well below the FCC SAR limits. The simple design, miniaturized profile, low SAR and wide operating bands with multiple band notches make the presented antenna an appealing choice for several UWB wearable body area network (WBAN) applications.

Securing safe driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern, despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Diverse malicious network attacks are ubiquitous, along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks. Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security. In this article we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), which offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information. Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs. Then we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proof-of-stake consensus mechanism. Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes. Finally, we discuss several open challenges that need to be addressed in future CAV networks., This article has been accepted for publication by IEEE Network. Copyright may be transferred without notice, after which this version may no longer be accessible

Based on people’s enthusiasm for swimming and their pursuit of swimming skill at a higher level, this paper proposes a non-contact motion guidance system based on optical motion capture technology, which aims to improve their swimming technique conveniently, scientifically and quickly. The swimming actions can be captured using one smartphone underwater, and the app on the phone will process the video and give suggestions based on the reference postures derived from statistics of professional swimmers for improvement of the swimming performance. In this system, the Canny operator was applied to detect the edge of the image captured from the swimming video, and the Hough line was implemented to recognize and locate the key position information which refers to the angle between knee and hip joints, and the angle between the trunk and horizontal plane. Furthermore, by comparing the key position information with the data of standard swimming, improvement measures can be given to improve the swimming skills of swimmers. As the result of the experiments, the system can accurately capture the swimmer’s movement state, locate the incorrect posture of the swimmer, and provide the corrective information in real-time. Although the paper only took breaststroke as an example, the proposed method can be extended to the other swimming styles.

Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this paper investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks., This paper has been submitted to IEEE Transactions on Vehicular Technology for peer review

The rise of intelligent reflecting surfaces (IRSs) is opening the door for unprecedented capabilities in visible light communication (VLC) systems. By controlling light propagation in indoor environments, it is possible to manipulate the channel conditions to achieve specific key performance indicators. In this paper, we investigate the role that IRSs can play in boosting the secrecy capacity of non-orthogonal multiple access (NOMA) VLC systems. More specifically, we propose an IRS-based physical layer security (PLS) mechanism that mitigates the information leakage risk inherent in NOMA. Our results demonstrate that the achieved secrecy capacity can be enhanced by up to 105% for a number of 80 IRS elements. To the best of our knowledge, this is the first paper that examines the PLS of NOMA-based IRS-assisted VLC systems.

The detail is available in the manuscript

In a space-air-ground integrated network (SAGIN), managing resources for the growing number of highly-dynamic and heterogeneous radios is a challenging task. Symbiotic communication (SC) is a novel paradigm, which leverages the analogy of the natural ecosystem in biology to create a radio ecosystem in wireless networks that achieves cooperative service exchange and resource sharing, i.e., service/resource trading, among numerous radios. As a result, the potential of symbiotic communication can be exploited to enhance resource management in SAGIN. Despite the fact that different radio resource bottlenecks can complement each other via symbiotic relationships, unreliable information sharing among heterogeneous radios and multi-dimensional resources managing under diverse service requests impose critical challenges on trusted trading and intelligent decision-making. In this article, we propose a secure and smart symbiotic SAGIN (S^4) framework by using blockchain for ensuring trusted trading among heterogeneous radios and machine learning (ML) for guiding complex service/resource trading. A case study demonstrates that our proposed S^4 framework provides better service with rational resource management when compared with existing schemes. Finally, we discuss several potential research directions for future symbiotic SAGIN., 8 pages, 6 figures, submitted to IEEE Network

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely WIreless SEmantic deliveRy for VR (WiserVR), for delivering consecutive 360{\deg} video frames to VR users. Specifically, deep learning-based multiple modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guarantee adequate transmission reliability and resilience under various channel states. Moreover, implementation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR., Comment: This magazine article has been accepted for publication by IEEE Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessible

No abstract available.

Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s). The advancements in different technologies like augmented reality, virtual reality, extended reality (XR), artificial intelligence (AI), and 5G/6G communication will be the key enablers behind the realization of AI-XR metaverse applications. While AI itself has many potential applications in the aforementioned technologies (e.g., avatar generation, network optimization, etc.), ensuring the security of AI in critical applications like AI-XR metaverse applications is profoundly crucial to avoid undesirable actions that could undermine users' privacy and safety, consequently putting their lives in danger. To this end, we attempt to analyze the security, privacy, and trustworthiness aspects associated with the use of various AI techniques in AI-XR metaverse applications. Specifically, we discuss numerous such challenges and present a taxonomy of potential solutions that could be leveraged to develop secure, private, robust, and trustworthy AI-XR applications. To highlight the real implications of AI-associated adversarial threats, we designed a metaverse-specific case study and analyzed it through the adversarial lens. Finally, we elaborate upon various open issues that require further research interest from the community., 24 pages, 11 figures

No abstract available.

Smart energy performance monitoring and optimisation at the supplier and consumer levels is essential to realising smart cities. In order to implement a more sustainable energy management plan, it is crucial to conduct a better energy forecast. The next-generation smart meters can also be used to measure, record, and report energy consumption data, which can be used to train machine learning (ML) models for predicting energy needs. However, sharing fine-grained energy data and performing centralised learning may compromise users' privacy and leave them vulnerable to several attacks. This study addresses this issue by utilising federated learning (FL), an emerging technique that performs ML model training at the user level, where data resides. We introduce FedTrees, a new, lightweight FL framework that benefits from the outstanding features of ensemble learning. Furthermore, we developed a delta-based early stopping algorithm to monitor FL training and stop it when it does not need to continue. The simulation results demonstrate that FedTrees outperforms the most popular federated averaging (FedAvg) framework and the baseline Persistence model for providing accurate energy forecasting patterns while taking only 2% of the computation time and 13% of the communication rounds compared to FedAvg, saving considerable amounts of computation and communication resources.

One of the most significant applications of Internet of Things are smart meters with wireless capabilities. Smart gas and electricity meters can capture half-hourly pricing and consumption data and send automated meter readings to your energy provider, in contrast to regular meters that can only register a running total of energy used. However, the legacy regular meters were not installed with wireless connectivity in mind and are usually found in hard-to-reach places for wireless radio coverage. To understand these scenarios, this paper provides signal strength measurements conducted at the Building Research Establishment determining building penetration losses in both 900 and 2,100 MHz band. We then present a building penetration loss model using these measurements that is practical and cost effective when compared to traditional statistical propagation loss models.

One of the most significant applications of Internet of Things are smart meters with wireless capabilities. Smart gas and electricity meters can capture half-hourly pricing and consumption data and send automated meter readings to your energy provider, in contrast to regular meters that can only register a running total of energy used. However, the legacy regular meters were not installed with wireless connectivity in mind and are usually found in hard-to-reach places for wireless radio coverage. To understand these scenarios, this paper provides signal strength measurements conducted at the Building Research Establishment determining building penetration losses in both 900 and 2,100 MHz band. We then present a building penetration loss model using these measurements that is practical and cost effective when compared to traditional statistical propagation loss models.

Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 THz to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.

This paper introduces handover (HO) skipping topology analysis that adjust the HO Skipping of 5G and beyond applications to improve the overall network performance and diminish negative effects. We propose a novel Poisson point process (PPP) based context-aware HO skipping approach to focus on the impact of HO metrics such as, passenger’s trajectory, different velocities, and a mean-time of a passenger within a BS to maintain a good quality of service (QoS). Our proposed scheme, context-aware HO skipping enables a dynamic HO skipping where the skipping decision is taken based on the load of the BSs along the passenger’s trajectory. The parameters have been analysed and implemented in a dynamic simulator and have been investigated for different parameter sets in a high-speed railway simulation scenario. Our simulation results in the robustness of the framework show comparable coverage probability on various high train velocities and mean-times.

Smart building technologies transform buildings into agile, sustainable, and health-conscious ecosystems by leveraging IoT platforms. In this regard, we have developed a Persuasive Energy Conscious Network (PECN) at the University of Glasgow to understand the user-centric energy consumption patterns in an agile workspace. PECN consists of desk-level energy monitoring sensors that enable us to develop user-centric models that can be exploited to characterize the normal energy usage behavior of an office occupant. In this study, we make use of staked long short-term memory (LSTM) to forecast future energy demands. Moreover, we employed statistical techniques to automate the detection of anomalous power consumption patterns. Our experimental results indicate that post-anomaly resolution leads to 6.37% improvement in the forecasting accuracy.

The dataset is about reading lips in a privacy preserving manner. In particular, radio frequency (RF) sensing was used to capture unique channel variation due to lip movements. USRP x300 was utilised equipped with the VERT2450 omnidirectional antenna and HyperLOG 7040 X used for reception and transmission respectively. Further, the same experiment was repeated with a Xethru UWB radar, where doppler frequency shifts due to lip movements are captured. We consider six classes for lip movements. Five vowels (a, e, i, o, u) and one empty class where lips were not moving. We are able to read lips even under face masks. Three subjects 1 male and 2 females participated in the experiments.

In recent years, research has focused on exploiting the inherent physical (PHY) characteristics of wireless channels to discriminate between different spatially separated network terminals, mitigating the significant costs of signature-based techniques. In this paper, the legitimacy of the corresponding terminal is firstly verified at the protocol stack’s upper layers, and then the re-authentication process is performed at the PHY-layer. In the latter, a unique PHY-layer signature is created for each transmission based on the spatially and temporally correlated channel attributes within the coherence time interval. As part of the verification process, the PHY-layer signature can be used as a message authentication code to prove the packet’s authenticity. Extensive simulation has shown the capability of the proposed scheme to support high detection probability at small signal-to-noise ratios. In addition, security evaluation is conducted against passive and active attacks. Computation and communication comparisons are performed to demonstrate that the proposed scheme provides superior performance compared to conventional cryptographic approaches.

Narrow Band Internet of Things (NB-IoT) is one of the drivers of industry 4.0 and the study of its wireless behavior in Indoor industrial environments has become important. This is because of the unfavorable conditions poised to wireless propagation as a result of the presence of heavy-duty equipment and the physical structure of industrial buildings. In this paper, an indoor industry was modeled to present some of the reflective characteristics and its effects on wireless propagation, particularly large-scale fading. The results obtained which include propagation paths, path loss, and impulse response showed how the environment affected the wireless transmission of NB-IoT. However, to mitigate this challenge, a collaborative scheme is introduced to improve the transmission among the affected NB-IoT terminals. The proposed scheme resulted in a collective improvement in path loss value by 30.44%.

Diagonal loading is one of the most widely used and effective methods to improve the robustness of both adaptive beamformers and Direction of Arrival (DOA) estimation due to the involvement of the sensor received covariance matrix. In addition, subspace-based DOA estimation techniques rely on multiple snapshots to achieve high estimation accuracies. This paper presents the study of a modified diagonally loaded sample covariance matrix for accurate DOA estimation in adverse scenarios. The proposed and novel technique deciphers poor DOA estimation in a low SNR environment by computationally changing the received sample covariance matrix. Our method is computationally simple as it does not require peak searching and does not depend on the coherency of the signal. The efficacy of the proposed method is examined via computer simulation for various sensor array sizes and the number of snapshot samples. Based on our numerical simulation results, our proposed method generally outperforms most state-of-the-art DOA estimators. In a finite number of snapshots and a single signal source, our proposed method performs 9.5% better than the state-of-the-art DOA estimation technique, 2.8% in multiple signal sources, and 8.5% in a single snapshot, single signal source environment of gained DOA estimation performance.

This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25$$\%$$ % improvement in the conditioning for the DoA estimation using the proposed technique.

The prosperity of artificial intelligence (AI) has laid a promising paradigm of communication system, i.e., intelligent semantic communication (ISC), where semantic contents, instead of traditional bit sequences, are coded by AI models for efficient communication. Due to the unique demand of background knowledge for semantic recovery, wireless resource management faces new challenges in ISC. In this paper, we address the user association (UA) and bandwidth allocation (BA) problems in an ISC-enabled heterogeneous network (ISC-HetNet). We first introduce the auxiliary knowledge base (KB) into the system model, and develop a new performance metric for the ISC-HetNet, named system throughput in message (STM). Joint optimization of UA and BA is then formulated with the aim of STM maximization subject to KB matching and wireless bandwidth constraints. To this end, we propose a two-stage solution, including a stochastic programming method in the first stage to obtain a deterministic objective with semantic confidence, and a heuristic algorithm in the second stage to reach the optimality of UA and BA. Numerical results show great superiority and reliability of our proposed solution on the STM performance when compared with two baseline algorithms., This work has been accepted for publication by 2022 IEEE International Conference on Computer Communications (INFOCOM). Copyright may be transferred without notice, after which this version may no longer be accessible

The spectrum efficiency can be greatly enhanced by the deployment of machine-to-machine (M2M) communications through cellular networks. Existing resource allocation approaches allocate maximum resource blocks (RBs) for cellular user equipments (CUEs). However, M2M user equipments (MUEs) share the same frequency among themselves within the same tier. This results in generating co-tier interference, which may deteriorate the MUE’s quality-of-service (QoS). To tackle this problem and improve the user experience, in this paper, we propose a novel resource utilization policy, which exploits reinforcement learning (RL) algorithm considering the pointer network (PN). In particular, we design an optimization problem that determines the optimal frequency and power allocation needed to maximize the achievable rate performance of all M2M pairs and CUEs in the network subject to the co-tier interference and QoS constraints. The proposed scheme enables the user equipment (UE) to autonomously select an available channel and optimal power to maximize the network capacity and spectrum efficiency while minimizing co-tier interference. Moreover, the proposed scheme is compared with traditional spectrum allocation schemes. Simulation results demonstrate the superiority of the proposed scheme than that of the traditional schemes. Moreover, the convergence of the proposed scheme is investigated which reduces the computational complexity (CC).

Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%.

By 2050, experts estimate that the agricultural produce must increase by 60%–70% to meet the needs of the ever increasing population of the world. To this aim, the concept of precision agriculture or smart farming has recently been coined. The idea of precision agriculture is well represented as a smart management system, having the ability to monitor, observe, sense, measure and control the health and water contents in plants at nano-scale and crops at macro-scale. The goal is to maximise the production while preserving the vital resources. The combination of terahertz (THz) based sensing technology to estimate plant health at a cellular level, and wireless sensor networks deployed within crops to monitor different variables while making intelligent decisions is far reaching. The integration and operation of such a macro-nano-sensor system requires a sustainable communication infrastructure that considers the demands of remote and agile agricultural environments. In this paper, an integrated sensing and communication system for plant health monitoring that utilises THz signals, is presented as a 6G use case. The joint architecture is outlined and various challenges including energy harvesting, practical implementation among others, followed by recommendations for future research are presented.

This paper introduces a 3D printed slotted waveguide linear antenna array at 28 GHz. The proposed antenna offers the advantages of low fabrication cost and reduced weight compared to the metallic slotted waveguide antenna array. The proposed design can achieve a high gain, low losses, and simplicity of fabrication. The proposed antenna geometry suggests an operating bandwidth of 15.8% (26.45 GHz – 31 GHz) suitable to fulfil 5G demands with a high gain performance of 12.0 dBi over the operating bandwidth. The proposed antenna structure is a suitable candidate for a variety of applications in the domain of 5G and beyond featuring cost-effectiveness, high gain, and very good performance overall.

The outbreak of the COVID-19 pandemic has caused unprecedented disruption to education and progressed remote teaching as a predominant model for delivering educational content. However, the online teaching and learning model has its challenges, such as the lack of technological tools to quantity the student attention and engagement with the learning content. This paper focuses on developing an e-learning framework for capturing and analysing the students’ attention during remote teaching sessions and subsequently profiling their learning behaviour leveraging eye-tracking data. Our proposed eye-tracking solution deploys a webcam to capture and track raw gaze points that grant the user the freedom of natural head movement and scalability compared to conventional eye-tracking approaches. We derived various gaze metrics in conjunction with state-of the-art machine learning (ML) models like logistic regression, support vector machine and polynomial regression to classify the student attention with an accuracy above 91%. Furthermore, our findings can help in the early detection and diagnosis of attention deficit hyperactivity disorder (ADHD) among students, thus supporting their learning journeys by creating an adaptive learning environment tailored to their needs.

Reconfigurable intelligent surfaces (RIS) have been recently proposed as an emerging technology to enhance wireless coverage. In this paper, we examine the performance of RIS-enabled unmanned aerial vehicle (UAV)-assisted communications in comparison with decode-and-forward (DF) relaying. Our results quantify the number of RIS elements required to outperform DF relaying in terms of the achievable rate. Additionally, we demonstrate effect of the UAV height on the total transmit power.

Fertility monitoring in humans for either natural conception or artificial insemination and fertilization has been a critical challenge both for the treating physician and the treated patients. Eggs in human female can be fertilized when they reach the Fallopian tube from the upper parts of the reproductive system. However, no technology, till date, on its own could detect the presence of eggs in the Fallopian tube and communicate their presence to the consulting physician or nurse and the patient, so that the next step can be initiated in a timely fashion. In this paper, we propose a conceptual architecture from a communications engineering point of view. The architecture combines intra-body nano-sensor network for detecting Fallopian tube activity, with body-area network for receiving information from the intra-body network and communicating the same over-the-air to the involved personnel (physician/nurse/patient). Preliminary simulations have been conducted using particle based stochastic simulator to investigate the relationship between achievable information rates, signal to noise ratio (SNR) and distance. It has been found that data rate as high as 300 Mbps is achievable at SNR 45. Hence, the proposed architecture ensures transfer of information with near-zero latency and minimum energy along with high throughput, so that necessary action can be taken within the short time-window of the Fallopian tube activity.

The next generation of health activity monitoring is greatly dependent on wireless sensing. By analysing variations in channel state information, several studies were capable of detecting activities in an indoor setting. This paper presents promising results of an experiment conducted to identify the activity performed by a subject and where it took place within the activity region. The system utilises two Universal Software Radio Peripheral (USRP) devices, operating as software-defined radios, to collect a total of 360 data samples that represent five different activities and an empty room. The five activities were performed in three different zones, resulting in 15 classes and a 16th class representing the room whilst it is empty. Using the Random Forest classifier, the system was capable of differentiating between the majority of activities, across the 16 classes, with an accuracy of almost 94%. Moreover, it was capable of detecting whether the room is occupied, with an accuracy of 100%, and identify the walking directions of a human subject in three different positions within the room, with an accuracy of 90%.

In the WiFi protocol, channel state information (CSI) is the modulated as the fine-grained data to assess the channel efficiency. Meanwhile, it contains the information about the environment change, including the movement of human in a specific environment. Therefore, the CSI data can be used to recognize the human activity. In this paper, we design a vision and WiFi collaboration-based human activity recognition scheme to classify the human activities. More specifically, we collect the CSI data from the WiFi signals and the human skeleton points from the video signals. Then, we construct a long-short-term Transformer network to build up the collaboration of the CSI data and the skeleton points. Based on this collaboration, we can use the CSI data to well recognize the human activities.

Real-time monitoring is an essential part in the development of healthcare monitoring systems. Research has shown that human movement affects the propagation of radio frequencies, as signals will reflect off the human body. Machine Learning techniques have been used in research to classify patterns observed in the signal propagation. This paper makes use of universal software radio peripheral devices to create a wireless communication link where the signal propagation data, known as channel state information, is collected while a user moves or remains still. A machine learning model which achieved an accuracy result of 93.25 % is used to classify between movement and no activity. Inference is then used to decide if the human position is sitting or standing and detected movements are used to differentiate between the two positions. The testbed implements cloud storage and a web-interface to present a visualisation of the human position.

Aiming at the self-association feature of the Hopfield neural network, we can reduce the\ud need for extensive sensor training samples during human behavior recognition. For a\ud training algorithm to obtain a general activity feature template with only one time data\ud preprocessing, this work proposes a data preprocessing framework that is suitable for\ud neuromorphic computing. Based on the preprocessing method of the construction matrix\ud and feature extraction, we achieved simplification and improvement in the classification of\ud output of the Hopfield neuromorphic algorithm. We assigned different samples to neurons\ud by constructing a feature matrix, which changed the weights of different categories to\ud classify sensor data. Meanwhile, the preprocessing realizes the sensor data fusion\ud process, which helps improve the classification accuracy and avoids falling into the\ud local optimal value caused by single sensor data. Experimental results show that the\ud framework has high classification accuracy with necessary robustness. Using the\ud proposed method, the classification and recognition accuracy of the Hopfield\ud neuromorphic algorithm on the three classes of human activities is 96.3%. Compared\ud with traditional machine learning algorithms, the proposed framework only requires\ud learning samples once to get the feature matrix for human activities, complementing\ud the limited sample databases while improving the classification accuracy.

With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety, suicide commitment, and mental disorder, particularly in the young adults who have excessively spent time on social media which necessitates a thorough psychological analysis of all these platforms. This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates. In this paper, we start with depression detection in the first instance and then expand on analyzing six other psychotic issues (e.g., depression, anxiety, psychopathic deviate, hypochondria, unrealistic, and hypomania) commonly found in adults due to extreme use of social media networks. To classify the psychotic issues with the user's mental state, we have employed different Machine Learning (ML) classifiers i.e., Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN). The used ML models are trained and tested by using different combinations of features selection techniques. To observe the most suitable classifiers for psychotic issue classification, a cost-benefit function (sometimes termed as ‘Suitability’) has been used which combines the accuracy of the model with its execution time. The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.

In this paper, we model, analyze and optimize the multi-user and multi-order-reflection (MUMOR) intelligent reflecting surface (IRS) networks. We first derive a complete MUMOR IRS network model applicable for the arbitrary times of reflections, size and number of IRSs/reflectors. The optimal condition for achieving sum-rate upper bound with one IRS in a closed-form function and the analytical condition to achieve interference-free transmission are derived, respectively. Leveraging this optimal condition, we obtain the MUMOR sum-rate upper bound of the IRS network with different network topologies, where the linear graph (LG), complete graph (CG) and null graph (NG) topologies are considered. Simulation results verify our theories and derivations and demonstrate that the sum-rate upper bounds of different network topologies are under a K-fold improvement given K-piece IRS.

This paper presents for the first time a compact three-port Lorentz resonance-based microstrip sensor for permittivity and moisture sensing, as well as refractive index comparison. The proposed sensor is verified by simulations and experiments. The resonant frequency of the designed sensor is 2.24 GHz. The proposed sensor has a quality factor of 102, sensitivity of 45.4 mm/refractive index unit, and figure-of-merit of 34.6. The sensitivity of the proposed sensor is quite superior to other resonance based sensors and a fair comparison is presented in this work. The proposed sensor can be used to find the permittivity of dielectric materials, the moisture content of plants, as well as can be used as a refractive index comparator.

This paper proposes a wideband miniaturized 3 dB 180° rat-race coupler. For ease of fabrication and measurement, the operating frequency of the 180° coupler is designed from 700-960 MHz. Hence, an operating bandwidth of around 30% is achieved at the center frequency of 830 MHz. A size reduction of 95% is accomplished along with a fractional bandwidth of 30%. A meander line configuration is adopted and gaps between the lines are optimized to enhance the capacitance of the structure. The proposed design is fabricated on Rogers 4360 substrate and measured results of the prototype show good agreement with simulated results.

In anticipation of a rapid increase in wireless communication, MIMO is one key technology to be explored for 5G. Conventional approaches are unable to predict many of the key characteristics for MIMO channels, and more detailed methods suffer from significant computational complexity due to the number of antennas in a MIMO array. In this work, the beamforming performance for moving users in a large cell with effective channel throughput has been explored. The Glasgow University campus model is used to estimate channel properties when various beamforming techniques are implemented. The techniques explored are Maximum Ratio Transmission (MRT) (for transmitter), Equal Gain Combining (EGC), Selection Combining (SC), and Max Ratio Combining (MRC) (for receiver) beamforming in 3GPP Long Term Evolution (LTE). Throughput, received signal strength, and signal to noise interference ratio (SINR) are determined. By implementing the beamforming techniques, on average, we are able to improve the throughput from 9 Mbps to 14 Mbps. The best throughput/SINR has been observed with MRT-MRC in comparison to No-beamforming.

This paper proposes a coplanar waveguide feed (CPW-fed) based patch antenna for implantable biomedical applications operating at Industrial, Scientific, and Medical (ISM) band. The antenna has a magnetically symmetric configuration with dimensions of 30 mm ×30 mm l. In free space, the proposed antenna covers a wideband ranging from 2.39 - 2.57 GHz, thereby posing a -10 dB bandwidth of 180 MHz with a peak gain of 2.23 dB. For implant case, the antenna is simulated by creating a three-layered human tissue model in CST MWS 2019. The proposed design also offers -10 dB bandwidth covering the ISM band after implanting inside muscle layer with a peak gain value of -6.46 dB. A prototype of antenna is fabricated for measurement proposes. An ex-vivo testing of proposed design was performed by placing the fabricated antenna inside a fresh pork slab. The measured and simulated results demonstrate the significance of proposed antenna for wearable and implantable biomedical applications.