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This article introduces a novel lightweight framework using ambient backscattering communications to counter eavesdroppers. In particular, our framework divides an original message into two parts. The first part, i.e., the active-transmit message, is transmitted by the transmitter using conventional RF signals. Simultaneously, the second part, i.e., the backscatter message, is transmitted by an ambient backscatter tag that backscatters upon the active signals emitted by the transmitter. Notably, the backscatter tag does not generate its own signal, making it difficult for an eavesdropper to detect the backscattered signals unless they have prior knowledge of the system. Here, we assume that without decoding/knowing the backscatter message, the eavesdropper is unable to decode the original message. Even in scenarios where the eavesdropper can capture both messages, reconstructing the original message is a complex task without understanding the intricacies of the message-splitting mechanism. A challenge in our proposed framework is to effectively decode the backscattered signals at the receiver, often accomplished using the maximum likelihood (MLK) approach. However, such a method may require a complex mathematical model together with perfect channel state information (CSI). To address this issue, we develop a novel deep meta-learning-based signal detector that can not only effectively decode the weak backscattered signals without requiring perfect CSI but also quickly adapt to a new wireless environment with very little knowledge. Simulation results show that our proposed learning approach, without requiring perfect CSI and complex mathematical model, can achieve a bit error ratio close to that of the MLK-based approach. They also clearly show the efficiency of the proposed approach in dealing with eavesdropping attacks and the lack of training data for deep learning models in practical scenarios.

This paper explores a multi-user multi-carrier system leveraging an active reconfigurable intelligent surface (RIS), where the joint design of the RIS's programmable reflecting elements and the subcarrier-wise beamformers at the base station is investigated. To overcome the limitation of the conventional design, which aims solely at sum-rate maximization resulting in zero rates for some users across all sub-carriers and thus failing to boost all users rates, we propose the two alternative designs: one maximizing the geometric mean of the users' rates (GM-rate maximization) and the other maximizing the soft users' minimum rate (soft max-min rate optimization). However, they pose challenges as large-scale nonconvex problems, rendering convex-solver computational approaches impractical. To tackle this, we develop iterative computational procedures based on closed-form expressions of scalable complexity. Extensive simulations demonstrate the substantial benefits of these novel designs in significantly enhancing multi-user rates. Notably, under the same power budget, the active-RIS-assisted multi-carrier system achieves approximately twice the minimum user-rate or sum rate compared to RIS-less or passive-RIS-assisted counterparts.

A network relying on a large antenna-array-aided base station is designed for delivering multiple information streams to multi-antenna users over high-frequency bands such as the millimeter-wave and sub-Terahertz bands. The state-of-the-art analog precoder (AP) dissipates excessive circuit power due to its reliance on a large number of phase shifters. To mitigate the power consumption, we propose a novel AP relying on a controlled number of phase shifters. Within this new AP framework, we design a hybrid precoder (HP) for maximizing the users’ minimum throughput, which poses a computationally challenging problem of large-scale, nonsmooth mixed discrete-continuous log-determinant optimization. To tackle this challenge, we develop an algorithm which iterates through solving convex problems to generate a sequence of HPs that converges to the max-min solution. We also introduce a new framework of smooth optimization termed soft max-min throughput optimization. Additionally, we develop another algorithm, which iterates by evaluating closed-form expressions to generate a sequence of HPs that converges to the soft max-min solution. Simulation results reveal that the HP soft max-min solution approaches the Pareto-optimal solution constructed for simultaneously optimizing both the minimum throughput and sum-throughput. Explicitly, it achieves a minimum throughput similar to directly maximizing the users’ minimum throughput and it also attains a sum-throughput similar to directly maximizing the sum-throughput.

By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), thus enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs' computing/communications resources to a given favorable set of users (e.g., closer to edge nodes) may block other devices from their services. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare or minimize the total energy consumption but do not consider the computing/battery status of each mobile device. This work develops an energy-based proportionally fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipments (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and continuous (resource allocation) variables. To tackle the NP-hard mixed integer optimization problem, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branch-and-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decisions and multiple subproblems (SPs) for resource allocation. To quickly eliminate inefficient offloading solutions, the MP is integrated with powerful Benders cuts exploiting the ENs' resource constraints. We then develop a dynamic branch-and-bound algorithm (DBB) to efficiently solve the MP considering the load balance among ENs. The SPs can either be solved for their closed-form solutions or be solved in parallel at ENs, thus reducing the complexity. The numerical results show that the DBBD returns the optimal solution in maximizing the proportional fa

With the ever increasing popularity of blockchain applications securing blockchain networks plays a critical role in these cyber systems In this paper we first study cyberattacks e g flooding of transactions brute pass in blockchain networks and then propose an efficient collaborative cyberattack detection model to protect blockchain networks Specifically we deploy a blockchain network in our laboratory to build a new dataset including both normal and attack traffic data The main aim of this dataset is to generate actual attack data from different nodes in the blockchain network that can be used to train and test blockchain attack detection models We then propose a realtime collaborative learning model that enables nodes in the network to share learning knowledge without disclosing their private data thereby significantly enhancing system performance for the whole network The extensive simulation and realtime experimental results show that our proposed detection model can detect attacks in the blockchain network with an accuracy of up to 97

This article investigates reconfigurable intelligent surface (RIS)-aided OFDM network, where a multiple-antenna aided base station (BS) transmits its downlink (DL) signals to multiple single-antenna users via an RIS, which consists of a considerable amount of low-resolution programmable reflecting elements (PREs). Explicitly, we propose the joint design of the multi-user (MU) beamformers and the RIS's PREs for quality-of-service target in terms of the individual users' rates. In the face of dispersive channels, we demonstrate that this poses a large-scale mixed discrete continuous optimization problem of intractable nature. We then tackle this challenge by developing low-complexity iterative procedures, which invoke light-weight closed-form expressions at each iteration, are developed for its computational solution. The simulations demonstrate their computational efficiency and additionally, reveal the deficiencies of the conventional MU OFDM beamforming design based on sum-rate maximization.

Scalable algorithms are conceived for obtaining the sum-rate capacity of the reconfigurable intelligent surface (RIS)-Aided multiuser (MU) multiple-input multiple-output (MIMO) broadcast channel (BC), where a multi-Antenna base station (BS) transmits signals to multi-Antenna users with the help of an RIS equipped with a massive number of finite-resolution programmable reflecting elements (PREs). As a byproduct, scalable path-following algorithms emerge for determining the sum-rate capacity of the conventional MIMO BCs, closing a long-standing open problem of information theory. The paper also develops scalable algorithms for maximizing the minimum rate (max-min rate optimization) of the users achieved by the joint design of RIS's PRE and transmit beamforming for such an RIS-Aided BC. The simulations provided confirm the high performance achieved by the algorithms developed, despite their low computational complexity.

This article aims to study intrusion attacks and then develop a novel cyberattack detection framework to detect cyberattacks at the network layer (e.g., brute password and flooding of transactions) of blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchain network will serve two purposes, i.e., to generate the real traffic data (including both normal data and attack data) for our learning models and to implement real-time experiments to evaluate the performance of our proposed intrusion detection framework. To the best of our knowledge, this is the first dataset that is synthesized in a laboratory for cyberattacks in a blockchain network. We then propose a novel collaborative learning model that allows efficient deployment in the blockchain network to detect attacks. The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, learn the knowledge from data using the Deep Belief Network, and then share the knowledge learned from its data with other blockchain nodes in the network. In this way, we can not only leverage the knowledge from all the nodes in the network but also do not need to gather all raw data for training at a centralized node like conventional centralized learning solutions. Such a framework can also avoid the risk of exposing local data’s privacy as well as excessive network overhead/congestion. Both intensive simulations and real-time experiments clearly show that our proposed intrusion detection framework can achieve an accuracy of up to 98.6% in detecting attacks.

With the increasing congestion in lower frequency bands of satellite spectrum the integration of cognitive radio technology is expected to become essential for efficient spectrum sharing in both Geostationary and Low Earth Orbit satellite systems Intelligent spectrum sharing strategies enable more efficient and effective satellite communications leading to increased capacity and improved performance In this paper we provide an overview of incorporating cognitive radio technology into dual satellite communications applications Moreover this paper presents a measurement apparatus for identifying spectral opportunities and presents preliminary results for such opportunities in a portion of the L band used by the Inmarsat Broadband Global Area Network downlink Numerous opportunities in both the time and frequency domains were identified for the spectrum surveyed The results revealed that almost half of the spectrum was available for over 99 of the capture duration with only 8 being available less than 1 of the time

Unmanned aerial vehicles (UAVs)-assisted wireless sensor networks (WSNs) have tremendous potential applications due to their flexibility and rapid deployment. Hybrid time-of-arrival (TOA) and angle-of-arrival (AOA) localization techniques are commonly used due to their high accuracy. The conventional TOA/AOA localization algorithms require both zenith and azimuth angle estimations at each agent. Since a zenith-and-azimuth AOA estimation requires an L-shape antenna array and complicated 2-D signal processing, conventional TOA/AOA algorithms lead to both hardware and computational complexity and high power consumption at each agent. This letter proposes a hybrid TOA/AOA localization algorithm, named $\text{T1A}_{a}$, to reduce the agents' complexity. $\text{T1A}_{a}$ only combines TOA-ranging with the azimuth angle estimation. Thereby, agents only require a 1-D antenna array, less complicated signal processing, and thus, lower power consumption. Such improvements are important for power-limited WSNs and Internet-of-Things (IoT) systems. Simulations are conducted to prove that the proposed $\text{T1A}_{a}$ technique can achieve similar performance as conventional TOA/AOA methods, while significantly simplifying agents' complexity.

Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically. To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.

Due to its security, transparency, and flexibility in verifying virtual assets, blockchain has been identified as one of the key technologies for Metaverse. Unfortunately, blockchain-based Metaverse faces serious challenges such as massive resource demands, scalability, and security/privacy concerns. To address these issues, this paper proposes a novel sharding-based blockchain framework, namely MetaShard, for Metaverse applications. Particularly, we first develop an effective consensus mechanism, namely Proof-of-Engagement, that can incentivize MUs' data and computing resource contribution. Moreover, to improve the scalability of MetaShard, we propose an innovative sharding management scheme to maximize the network's throughput while protecting the shards from 51% attacks. Since the optimization problem is NP-complete, we develop a hybrid approach that decomposes the problem (using the binary search method) into sub-problems that can be solved effectively by the Lagrangian method. As a result, the proposed approach can obtain solutions in polynomial time, thereby enabling flexible shard reconfiguration and reducing the risk of corruption from the adversary. Extensive numerical experiments show that, compared to the state-of-the-art commercial solvers, our proposed approach can achieve up to 66.6% higher throughput in less than 1/30 running time. Moreover, the proposed approach can achieve global optimal solutions in most experiments.

This letter presents a novel approach for spectrum sensing in cognitive satellite-terrestrial networks. The approach uses multi-agent deep reinforcement learning (DRL) and reconfigurable intelligent surface to address the problem of under-utilization of terrestrial network spectrum in remote areas. Unlike previous studies that rely only on current sensing data, this approach utilizes historical data to improve spectrum detection accuracy and post-decision state to accelerate agent learning speed. Simulation results show that it outperforms existing DRL methods in terms of faster agent learning convergence and more effective detection of primary network spectrum occupancy.

In this paper, we propose FedChain, a novel framework for federated-blockchain systems, to enable effective transferring of tokens between different blockchain networks. Particularly, we first introduce a federated-blockchain system together with a cross-chain transfer protocol to facilitate the secure and decentralized transfer of tokens between chains. We then develop a novel PoS-based consensus mechanism for FedChain, which can satisfy strict security requirements, prevent various blockchain-specific attacks, and achieve a more desirable performance compared to those of other existing consensus mechanisms. Moreover, a Stackelberg game model is developed to examine and address the problem of centralization in the FedChain system. Furthermore, the game model can enhance the security and performance of FedChain. By analyzing interactions between the stakeholders and chain operators, we can prove the uniqueness of the Stackelberg equilibrium and find the exact formula for this equilibrium. These results are especially important for the stakeholders to determine their best investment strategies and for the chain operators to design the optimal policy to maximize their benefits and security protection for FedChain. Simulations results then clearly show that the FedChain framework can help stakeholders to maximize their profits and the chain operators to design appropriate parameters to enhance FedChain’s security and performance.

This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision process-based framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

Internet of Things, or IoT has been playing an important part in human’s lives. Nonetheless, the rapid growth of IoT-based services may lead to an increase in IoT-based attacks that compromise sensitive user data. IoT-based attack detection and prevention is thus essential for the development of IoT-based network technologies. However, this is a difficult task because the data transmitted between IoT devices is often complex and heterogeneous while IoT-based threats/attacks change rapidly over time. Machine learning-based threat detection methods, which are trained with old/existing labelled data, may become less effective in the future. For that, it is critical to develop effective learning models that can leverage both labelled and unlabelled data to timely adapt with the fast-varying attack methods. To this end, this chapter proposes a Deep Transfer Learning (DTL) model to build an effective IoT attack detection model from both labelled and unlabelled data collected from multiple IoT devices. The proposed solution combines the Multi-Maximum Mean Discrepancy (M2D) distance and two AutoEncoder (AE) networks named as Multi-Maximum Mean Discrepancy AutoEncoder (M2DA). The first AE is trained on labelled IoT data, while the second AE is trained on unlabelled IoT data. The purpose of the training process is to transfer the learned label information from the first AE to the second AE by the M2D distance. As a result, the second AE can efficiently classify and detect anomaly (attacks) even on unlabelled IoT network data. We further study the performance of the proposed M2DA framework using nine well-known commercial IoT datasets with different botnet families and attacks.

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.

Unlike conventional anti-eavesdropping methods that always require additional energy or computing resources (e.g., in friendly jamming and cryptography-based solutions), this work proposes a novel anti-eavesdropping solution that comes with mostly no extra power nor computing resource requirement. This is achieved by leveraging the ambient backscatter technology in which secret information can be transmitted by backscattering it over ambient radio signals. Specifically, the original message at the transmitter is first encoded into two parts: (i) active transmit message and (ii) backscatter message. The active transmit message is then transmitted by using the conventional wireless transmission method while the backscatter message is transmitted by backscattering it on the active transmit signals via an ambient backscatter tag. As the backscatter tag does not generate any active RF signals, it is intractable for the eavesdropper to detect the backscatter message. Therefore, secret information, e.g., a secret key for decryption, can be carried by the backscattered message, making the adversary unable to decode the original message. Simulation results demonstrate that our proposed solution can significantly enhance security protection for communication systems.

Integrated Communications and Sensing (ICS) has recently emerged as an enabling technology for ubiquitous sensing and IoT applications. For ICS application to Autonomous Vehicles (AVs), optimizing the waveform structure is one of the most challenging tasks due to strong influences between sensing and data communication functions. Specifically, the preamble of a data communication frame is typically leveraged for the sensing function. As such, the higher number of preambles in a Coherent Processing Interval (CPI) is, the greater sensing task’s performance is. In contrast, communication efficiency is inversely proportional to the number of preambles. Moreover, surrounding radio environments are usually dynamic with high uncertainties due to their high mobility, making the ICS’s waveform optimization problem even more challenging. To that end, this paper develops a novel ICS framework established on the Markov decision process and recent advanced techniques in deep reinforcement learning. By doing so, without requiring complete knowledge of the surrounding environment in advance, the ICS-AV can adaptively optimize its waveform structure (i.e., number of frames in the CPI) to maximize sensing and data communication performance under the surrounding environment’s dynamic and uncertainty. Extensive simulations show that our proposed approach can improve the joint communication and sensing performance up to 46.26% compared with other baseline methods.

This paper focuses on a network scenario where a multi-antenna access point serves multiple single-antenna users in the presence of multiple eavesdroppers, with the aid of a reconfigurable intelligent surface (RIS). The RIS employs low-resolution programmable reflecting elements (PREs) for cost-effective implementation. In order to establish secure links for all users, we consider the joint design of the transmit beamformers and PREs to maximize either the geometric mean of secrecy rates or the worst user's secrecy rate. Novel computational algorithms of low computational complexity are developed for the solution of these mixed discrete continuous optimization problems. Simulations show the merit of the proposed designs in in achieving fair secrecy rate distributions and ensuring secure links for all users.

Federated Learning FL with quantization and deliberately added noise over wireless networks is a promising approach to preserve the user differential privacy while reducing the wireless resources Specifically an FL learning process can be fused with quantized Binomial mechanism based updates contributed by multiple users to reduce the communication overhead cost as well as to protect the privacy of participating users However the optimization of wireless transmission and quantization parameters e g transmit power bandwidth and quantization bits as well as the added noise while guaranteeing the privacy requirement and the performance of the learned FL model remains an open and challenging problem In this paper we aim to jointly optimize the level of quantization parameters of the Binomial mechanism and devices transmit powers to minimize the training time under the constraints of the wireless networks The resulting optimization turns out to be a Mixed Integer Non linear Programming MINLP problem which is known to be NP hard To tackle it we transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one We then propose an approximate algorithm that can solve the transformed problem with an arbitrary relative error guarantee Intensive simulations show that for the same wireless resources the proposed approach achieves the highest accuracy close to that of the conventional FL with no quantization and no noise added This suggests the faster convergence training time of the proposed wireless FL framework while optimally preserving users privacy

Latent graph structure and stimulus of graph-structured data contain critical private information, such as brain disorders in functional magnetic resonance imaging data, and can be exploited to identify individuals. It is critical to perturb the latent information while maintaining the utility of the data, which, unfortunately, has never been addressed. This paper presents a novel approach to obfuscating the latent information and maximizing the utility. Specifically, we first analyze the graph Fourier transform (GFT) basis that captures the latent graph structures, and the latent stimuli that are the spectral-domain inputs to the latent graphs. Then, we formulate and decouple a new multi-objective problem to alternately obfuscate the GFT basis and stimuli. The difference-of-convex (DC) programming and Stiefel manifold gradient descent are orchestrated to obfuscate the GFT basis. The DC programming and gradient descent are employed to perturb the spectral-domain stimuli. Experiments conducted on an attention-deficit hyperactivity disorder dataset demonstrate that our approach can substantially outperform its differential privacy-based benchmark in the face of the latest graph inference attacks.

Full-dimensional (FD) multi-user massive multiple-input multiple output (m-MIMO) systems employ large two-dimensional (2D) rectangular antenna arrays to control both the azimuth and elevation angles of signal transmission. We introduce the sum of two outer products of the azimuth and elevation beamforming vectors having moderate dimensions as a new class of FD beamforming. We show that this low-complexity class is capable of outperforming 2D beamforming relying on the single outer product of the azimuth and elevation beamforming vectors. It is also capable of performing close to its FD counterpart of massive dimensions in terms of either the users' minimum rate or their geometric mean rate (GM-rate), or sum rate (SR). Furthermore, we also show that even FD beamforming may be outperformed by our outer product-based improper Gaussian signaling solution. Explicitly, our design is based on low-complexity algorithms relying on convex problems of moderate dimensions for max-min rate optimization or on closed-form expressions for GM-rate and SR maximization.

A wireless network serving multiple users in the millimeter-wave or the sub-terahertz band by a base station is considered. High-throughput multi-user hybrid-transmit beamforming is conceived by maximizing the minimum rate of the users. For the sake of energy-efficient signal transmission, the array-of-subarrays structure is used for analog beamforming relying on low-resolution phase shifters. We develop a convex-solver based algorithm, which iteratively invokes a convex problem of the same beamformer size for its solution. We then introduce the soft max-min rate objective function and develop a scalable algorithm for its optimization. Our simulation results demonstrate the striking fact that soft max-min rate optimization not only approaches the minimum user rate obtained by max-min rate optimization but it also achieves a sum rate similar to that of sum-rate maximization. Thus, the soft max-min rate optimization based beamforming design conceived offers a new technique of simultaneously achieving a high individual quality-of-service for all users and a high total network throughput.

Although Rate Splitting Multiple Access RSMA is a promising scheme to effectively manage interference and enhance data rate and spectral utilization its applications for Virtual Reality VR streaming have not been well studied In addition to the strict latency requirement as in conventional High Definition streaming VR streaming further requires more computing resources at the transmitter to promptly react to the dynamic of users Field of View interests Unfortunately current conventional RSMA approaches could not effectively handle these problems since they are not intentionally developed to deal with the special features of VR streaming To address these challenges we first propose a novel hierarchical multicast technique to effectively integrate the RSMA and VR streaming by exploiting the Field of Views of VR users Then the VR streaming problem established based on RSMA is formulated as a joint computation and communication optimization problem which can not only guarantee VR streaming latency requirement but also effectively manage interferences among users Finally due to the dynamic and uncertainty of wireless channels and users demands we develop a deep reinforcement learning approach to find the optimal policy for the system This learning solution allows us to find the optimal parameters for the system via trail and error learning process and thus it is effective in dealing with the uncertainty and unknown information from surrounding environment Simulation results demonstrate that our proposed solution can satisfy the VR requirement of millisecond latency that is much lower than those of the baselines

A new hybrid amalgam of analog and baseband digital beamforming is conceived for millimeter-wave (mmWave) multi-user networks. The base station is equipped with a large-scale antenna-array, which however relies on a limited number of radio frequency chains for mitigating the severe path-loss of mmWave band transmission. Each user is also equipped with a multi-antenna array. The analog beamformer's weight-resolution is set low for the sake of power efficient implementation. The hybrid beamformer design relies on the novel optimization criterion of directly maximizing the geometric means of users' rates, which is shown to result in fair rate-distributions for the users without imposing a minimum user-rate constraint. Furthermore, new computationally efficient algorithms are developed, which are purely based on closed-form low-complexity expressions. Hence these low-complexity solutions are eminently suitable for large-scale mmWave arrays. Numerical examples are provided for demonstrating their efficiency.

This chapter helps the researchers aware of potential Metaverse applications in notable areas, including entertainment, virtual office, education, healthcare, autonomous vehicles, and virtual tourism. The entertainment industry is one of the most important pillars to pushing Metaverse's implementation forward. Several entertainment types have been integrated into a single virtual environment. The Metaverse introduces a seamless integration of multiple extended reality-based virtual worlds and limitless creativity for users. The Metaverse makes account management easier than that of traditional approaches. The chapter discusses some examples of entertainment activities in Metaverse. Virtual reality and augmented reality technologies can bring a seamless experience for Metaverse users as they can work from anywhere with their optimum desktop setup. The chapter shows how important it is to adapt new technology such as Metaverse to future e-learning. Metaverse can offer a great virtual environment for testing autonomous vehicles.

This is the first treatise on multi-user (MU) beamforming designed for achieving long-term rate-fairness in full-dimensional MU massive multi-input multi-output (m-MIMO) systems. Explicitly, based on the channel covariances, which can be assumed to be known beforehand, we address this problem by optimizing the following objective functions: the users’ signal-to-leakage-noise ratios (SLNRs) using SLNR max-min optimization, geometric mean of SLNRs (GM-SLNR) based optimization, and SLNR soft max-min optimization. We develop a convex-solver based algorithm, which invokes a convex subproblem of cubic time-complexity at each iteration for solving the SLNR max-min problem. We then develop closed-form expression based algorithms of scalable complexity for the solution of the GM-SLNR and of the SLNR soft max-min problem. The simulations provided confirm the users’ improved-fairness ergodic rate distributions.

Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks. However, the IDSs for IoT devices face inherent challenges of IoT systems, including the heterogeneity of IoT data/devices, the high dimensionality of training data, and the imbalanced data. Moreover, the deployment of IDSs on IoT systems is challenging, and sometimes impossible, due to the limited resources such as memory/storage and computing capability of typical IoT devices. To tackle these challenges, this article proposes a novel deep neural network/architecture called Constrained Twin Variational Auto-Encoder (CTVAE) that can feed classifiers of IDSs with more separable/distinguishable and lower-dimensional representation data. Additionally, in comparison to the state-of-the-art neural networks used in IDSs, CTVAE requires less memory/storage and computing power, hence making it more suitable for IoT IDS systems. Extensive experiments with the 11 most popular IoT botnet datasets show that CTVAE can boost around 1% in terms of accuracy and Fscore in detection attack compared to the state-of-the-art machine learning and representation learning methods, whilst the running time for attack detection is lower than 2E-6 seconds and the model size is lower than 1 MB. We also further investigate various characteristics of CTVAE in the latent space and in the reconstruction representation to demonstrate its efficacy compared with current well-known methods.

In this article, a compact multiservice antenna (MSA) is presented for sensing and communication using a reconfigurable complementary spiral resonator. A three turns complementary spiral resonator (3-CSR) is inserted in the ground plane of a modified patch antenna to create a miniaturized structure. Two positive-intrinsic-negative (p-i-n) diodes (D1 and D2) are also integrated with the 3-CSR to achieve frequency reconfiguration. The proposed structure operates in three different modes, i.e., dual-band joint communication and sensing antenna (JCASA), dual-band antenna, and single-band antenna. The required mode can be selected by changing the state of the p-i-n diodes. In mode-1, the first band (0.95-0.97 GHz) of the antenna is dedicated to sensing by using frequency-domain reflectometry (FDR), while the second band (1.53-1.56 GHz) is allocated to communication. The sensing ability of the proposed structure is utilized to measure soil moisture using FDR. Based on the frequency shift, the permittivity of the soil is observed to measure soil moisture. In mode-2 and mode-3, the structure operates as a standard dual- and single-band antenna, respectively, with a maximum gain of 1.5 dBi at 1.55 GHz. The proposed planar structure, with its simple geometry and a high sensitivity of 1.7%, is a suitable candidate for precision farming. The proposed structure is versatile and capable of being utilized as a single- or dual-band antenna and also measuring the permittivity of materials within the range of 1-20. Hence, it is adaptable to a range of applications.

This article presents a new graph-learning technique to accurately infer the graph structure of COVID-19 data, helping to reveal the correlation of pandemic dynamics among different countries and identify influential countries for pandemic response analysis. The new technique estimates the graph Laplacian of the COVID-19 data by first deriving analytically its precise eigenvectors, also known as graph Fourier transform (GFT) basis. Given the eigenvectors, the eigenvalues of the graph Laplacian are readily estimated using convex optimization. With the graph Laplacian, we analyze the confirmed cases of different COVID-19 variants among European countries based on centrality measures and identify a different set of the most influential and representative countries from the current techniques. The accuracy of the new method is validated by repurposing part of COVID-19 data to be the test data and gauging the capability of the method to recover missing test data, showing 33.3% better in root mean squared error (RMSE) and 11.11% better in correlation of determination than existing techniques. The set of identified influential countries by the method is anticipated to be meaningful and contribute to the study of COVID-19 spread.