Federated Learning serves as a distributed framework for machine learning. Traditional approaches to federated learning often assume the independence and identical distribution (IID) of client data. However, real-world scenarios frequently feature personalized characteristics in client data, deviating from the IID assumption. Additionally, challenges such as substantial communication overhead and limited resources at edge nodes hinder the practical implementation of federated learning. In response to the challenges in deploying federated learning, including uneven data distribution, communication bottlenecks, and resource limitations at edge nodes, this paper introduces an individualized federated learning framework based on model pruning. This framework effectively adapts the client's local model to the personalized distribution of local data while meeting the model aggregation requirements on the server. Utilizing sparse operations, the framework achieves personalized model pruning, efficiently compresses model parameters, and reduces computational load on edge nodes. Presently, our approach demonstrates a compression ratio of 3.8% on the non-IID dataset Feminist without compromising final training accuracy, resulting in a 12.3% acceleration in training speed.

An analytical model for an antenna-embedded wall, also called signal-transmissive wall, is presented in this work. In the signal-transmissive wall, multiple antenna elements are distributed periodically on both wall sides, and connected back-to-back through coaxial cables. Numerical full-wave simulations of the signal-transmissive wall are computationally demanding due to the fine meshes required in the cables while having an electrically large wall size. Therefore the simulations above 8 GHz are not feasible even with a powerful cluster computer of the authors' research site. The analytical model is an attractive alternative to the full-wave simulation of the wall, which combines the individual transmission characteristics of the bare wall, realized gains of antenna elements and cable losses. The analytical model accurately reproduces the full-wave simulated transmission coefficient of the signal-transmissive wall up to 8 GHz for arbitrary polarizations and incident angles of a plane wave. The model therefore allows analyses of the signal-transmissive wall beyond 8 GHz, showing more than 70 dB reduction of the transmission loss at 30 GHz compared to a bare wall.

Recent research has proposed media-based modulation (MBM) as a method to reduce the hardware complexity of wireless communications systems and therefore also achieve a reduction of the associated cost. In this work, we propose an MBM system based on a novel asymmetric signal constellation consisting of scaled and shifted Eisenstein integers. The constellation is generated by phase shifts induced by a reconfigurable intelligent antenna, where the magnitudes are modulated by turning on or off certain numbers of reflecting elements. At the receiver, a uniform linear antenna array is used to capture the incident electromagnetic planar wave. Robust estimation techniques, such as the median, the Weiszfeld algorithm, and the Sq-estimator are employed to recover the constellation points. A novel gain control scheme is proposed together with a phase offset detection method based on circular cross-correlation. Furthermore, complex-valued convolutional neural networks are used to decode the received signal. We also consider the performance of our system under impulse noise caused by voltage transients in addition to additive white Gaussian noise and show superior performance visa vie a generic 64-QAM modulation scheme and a brute-force arithmetic method based on the four-quadrant arctan function and the median.

A document by Anders Buvarp . Click on the document to view its contents.

Multiuser gigantic-multiple-input multiple-output (MU-gMIMO) and nonorthogonal multiple access (NOMA) are jointly seen as important enabling technologies for sixth generation (6G) networks. They have many benefits, such as spatial multiplexing, spatial diversity, massive connectivity, and spectral efficiency (SE). However, gMIMO-NOMA suffers from many inherent challenges. In this paper, we propose an MU-gMIMO-hybrid multiple-access (HMA) heterogeneous network architecture to address the 'nearly same channel gain' issue. Then, an iterative minimal mean squared error (IMMSE) scheme is applied along with quadrature amplitude modulation (QAM) for maximal ratio transmission in the proposed transceiver design to address the 'residual error' caused by imperfect successive interference cancellation (ISIC). Finally, to assess the performance of the proposed architecture for the 'time offsets' issue, we investigate the asynchronous mode with an MMSE detector matrix following imperfect channel state information (ICSI) to provide a new analysis for HMA transmission and formulate an optimization problem for energy efficiency (EE).

We elaborate the effects of carrier heating on the mobility characteristics of MOSFET inversion layers in the range of interest for devices of practical technological import (transverse fields ET ≈ 0.1 to 1 MV/cm), insofar as they are underpinned and governed by energy and momentum conservation principles. Carrier heating relatively to their surrounding lattice begins when the transverse (gate) electric field ET approaches the saturation field of carriers' thermal velocity, a material property. Additional energy imparted to carriers by yet higher fields causes their mean velocity, hence their longitudinal velocity component, to fall as required by momentum conservation. Whereas energy is converted predominantly from potential to kinetic form at relatively low fields where the flow of carriers is quasi-laminar, potential energy is converted (lost) to thermal energy in the saturated velocity range. The degradation of mobility is thermally-mediated: it 'emerges' by virtue of nonlinear interactions between transverse field, saturation velocity, and carrier heating, and is constrained by overarching conservation laws. Our findings indicate that the rate of carrier heating is a function only of interface terrain 'roughness' amplitude and fundamental constants, hence that the influence of surface orientation is ancillary to the heating phenomenon. Our analytical results are surprisingly simple, intuitively appealing, and in uniform agreement with Takagi et al's extensive observations of long standing, when the latter are apprehended in light of the unifying percepts and order herein set forth. As such, they readily inform the modeling of silicon and silicon carbide integrated MOSFET technologies. 1 Which has ostensibly become all but synonymous with "universal" in the pertinent literature. " As levels of complexity mount, … new properties arise as results and interconnections emerging at each new level. A higher level cannot be fully explained by taking it apart into component elements and rendering their properties in the absence of these interactions." [1]

In this study, we focus on an MCN where the direct links towards a shore BS are not available, due to excessive fading conditions. For this case, we use a UAV swarm to provide improved wireless connectivity, adopting non-orthogonal multiple access (NOMA) for high resource efficiency. In downlink communication, UAVs take into consideration the desired service rate and the channel quality of their links towards the maritime nodes. In the uplink, UAVs employ dynamic decoding ordering to enhance the performance of successive interference cancellation, avoiding fixed ordering of the maritime nodesâ\euro™ signals. Moreover, to ensure highly flexible UAV selection, UAVs have buffers to store data. Performance comparisons show that the UAV swarm-aided MCN enjoys increased average sum-rate by relying on multi-criteria-based interference cancellation and buffer-aided UAVs, over other benchmark schemes in the downlink and uplink.

The transition from 5G to 6G will result in an explosion of Internet of Things (IoT) devices that provide pervasive and constant connection across all spheres of human activities. In this regard, non-terrestrial networks (NTNs) will play a crucial role in supporting and supplementing terrestrial systems in order to cope with such a vast number of IoT devices and to fulfill the massive capacity needs of the most sophisticated of them. This study investigates the implication of several multiple access techniques for 6G enabled NTN-assisted IoT technologies. First, general architecture of NTN, NTN-assisted IoT technologies, and key features and challenges of NTNs are presented. Then, different types of multiple access schemes are discussed and compared in the context of NTN-assisted IoT systems. Simulation results are presented and important performance parameters such as energy efficiency and spectrum efficiency are examined for the discussed multiple access schemes. Furthermore, challenges and future research directions are discussed.

Recent trends in emerging applications have motivated researchers to design advanced wireless systems to meet their evolving requirements. These emerging applications include digital healthcare, intelligent transportation systems, Industry 5.0, and more. To address the evolving requirements, leveraging a metaverse to empower $6$G wireless systems is a viable solution. A metaverse-empowered $6$G wireless system can offer numerous benefits, but it may also be vulnerable to a wide variety of security attacks. In this survey, we discuss potential security attacks in metaverse-empowered $6$G wireless systems. We introduce an architecture designed to enhance security within metaverse-empowered $6$G wireless systems. This architecture comprises two key spaces: the meta space and the physical space. We present physical space attacks and outline effective solutions to secure metaverse-empowered $6$G wireless systems. We provide invaluable insights and discussions on meta space attacks, along with promising solutions. Finally, we discuss open challenges and provide future recommendations.

We introduce a new construction method of diffusion layers for Substitution Permutation Network (SPN) structures along with its security proofs. The new method can be used in block ciphers, stream ciphers, hash functions, and sponge constructions. Moreover, we define a new stream cipher mode of operation through a fixed pseudorandom permutation and provide its security proofs in the indistinguishability model. We refer to a stream cipher as a Small Internal State Stream (SISS) cipher if its internal state size is less than twice its key size. There are not many studies about how to design and analyze SISS ciphers due to the criterion on the internal state sizes, resulting from the classical tradeoff attacks. We utilize our new mode and diffusion layer construction to design an SISS cipher having two versions, which we call DIZY. We further provide security analyses and hardware implementations  of DIZY. In terms of area cost, power, and energy consumption, the hardware performance is among the best when compared to some prominent stream ciphers, especially for frame-based encryptions that need frequent initialization. Unlike recent SISS ciphers such as Sprout, Plantlet, LILLE, and Fruit; DIZY does not have a keyed update function, enabling efficient key changing.Â

Sixth-generation wireless technology (6G) introduces a paradigm shift and fundamental transformation of digital wireless connectivity by converging pillars of softwarization, virtualization, and wireless networks. Terahertz (THz) communication technologies are predicted to become more significant in 6G applications as the requirement for bandwidth grows and wireless cell sizes shrink. As a result, 6G will be able to deal with and manage numerous devices and services requiring enhanced spectral throughput and efficiently work with high interference levels. This convergence highlights the increased threat surface of 6G networks and the potentially severe impacts of sophisticated cyber incidents. Furthermore, the heterogeneity of connected devices and provided services will generate a huge amount of data to be processed and managed efficiently. Quantum computing (QC) can efficiently solve several 6G computationally hard problems with a quadratic speedup and provide adaptive techniques for controlling the current and future significant security threats of the 6G network. This article will discuss the role of various QC components on 6G and explore the opportunities and challenges to achieving such transformation.

This paper investigates the pivotal role of artificial languages, with a specific focus on BILA, in addressing challenges in Human-Robot Interaction (HRI). By delving into the complexities of speech recognition and technological constraints, the study introduces BILA as a groundbreaking solution. The research encompasses the design, implementation, and evaluation phases, emphasizing its transformative potential in enhancing user-robot interactions.

Ray-tracing is a promising alternative for Radio Frequency Planning particularly in urban areas. There are two fundamental techniques used for ray-tracing namely Shooting and Bouncing Rays and Method of Images. In this paper, we focus on the former and present simulation results for an urban scenario in the city of Helsinki. We also give an insight into how the Shooting and Bouncing Ray method can be implemented using basic linear algebra techniques. We show that ray-tracing can be used to evaluate the performance improvement attained through electromagnetic reflectors. Finally, we close the discussion by outlining the existing challenges and the way forward.

This paper provides a review of Extended Reality (ER) technologies and applications. The different technologies related to ER, such as Virtual Reality, Augmented Reality and Mixed Reality are introduced and explained, following an historical perspective. The basic technical concepts behind these technologies are briefly explained. Current applications are presented, including references to actual tools and systems, in order to clarify the concepts and the potential of the technologies.Â

Deepfakes the term was coined in 2018 by a Reddit user who created a Reddit forum dedicated to the creation and use of deep learning software for synthetically face swapping female celebrities into pornographic videos. According to Sumsub’s research in 2023, the top-5 identity fraud types in 2023 are AI-powered fraud, money muling networks, fake IDs, account takeovers, and forced verification. The country most attacked by deepfakes is Spain; the most forged document worldwide is the UAE passport, whereas Latin America is the region where fraud has increased in every country. On November 24, 2023, the Union Government of India issued an advisory to social media intermediaries to identify misinformation and deepfakes. A deepfake refers to a specific kind of synthetic media where a person in an image or video is swapped with another person’s likeness. AI-generated deepfakes have emerged as a complex and pervasive challenge in today’s digital landscape, enabling the creation of remarkably convincing yet falsified multimedia content. This review paper examines the multifaceted landscape of deepfakes, encompassing their technological underpinnings, societal implications, detection methodologies, and ethical considerations. The review aggregates and synthesizes a broad array of scholarly articles, studies, and reports to elucidate the diverse typologies of deepfakes, including face-swapping, voice cloning, and synthetic media, while delineating the intricate methodologies employed in their fabrication. This review culminates in an overview of future directions and recommendations, advocating for proactive measures to counter the escalating threat posed by AI-generated deepfakes.

Affective virtual reality (VR) gaming systems rely on timely physiological data collection, in order to generate personalized responses that enhance the emotional impact of a video game on its user. In this context, we propose a simple policy for timely data collection from wireless sensor nodes placed on the human body. Our policy is applied at each sensor node. Upon each packet arrival we check whether the buffer is full or not. If the buffer is full, then we empty it before adding the packet. In this very simple way, we avoid buffer congestion and impose timeliness. We simulated this aggressive buffer reset policy using a body area network (BAN) model in OMNeT++. By varying the packet generation rate of each node, we showed that our policy outperforms first come first served (FCFS) and last come first served (LCFS) queueing policies in terms of peak age, while packet reception is barely affected. Buffer resets can be easily integrated into existing random access protocols to support timely data collection.

Data cooperatives with fiduciary obligations to members provide a useful source of truthful information regarding a given member whose personal data is managed by the cooperative. Since one of the main propositions the cooperative model is to protect the data privacy of members, we explore the notion of blinded attestations in which the identity of the subject is removed from the attestations issued by the cooperative regarding one of its members. This is performed at the request of the individual member. We propose the use of a legal entity to countersign the blinded attestation, one that has an attorney-client relationship with the cooperative, and which can henceforth become the legal point of contact for inquiries regarding the individual related to the attribute being attested. There are several use-cases for this feature, including the Funds Travel Rule in transactions in digital assets, and the protection of privacy in decentralized social networks.

Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities – Prague and Munich. We focus on the robot’s ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system’s holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.

If tokenized assets are to be a reality in the future decentralized Web3 then transaction keys need to be distributed and under the control of the asset-owners. This requires a careful design of wallet systems based on trusted hardware. A core feature needed for wallet systems is the attestation of the state of the transaction keys in the wallet without disclosure of the keys. This feature is relevant for relying parties such as insurance providers who need to perform risk assessment based on the security quality of the environment inside the wallet system that is protecting the transaction keys. In the longer term, all key-bearing devices that participate in a decentralized tokenized asset network will need to be hardened using trusted hardware, with attestation capabilities for detecting and countering cyberattacks.

 X is a discrete memoryless binary source , Sequence X undergoes lossless transformation to satisfy the requirement â\euroœEach 1 is separated by one or more zeros“, ”Each 0 is separated by one or two 1s” or similar condition , These conditions are the decision conditions for error detection in channel transmission. A new channel coding method is proposed based on a weighted probability model for lossless coding , The encoding rate and encoding and decoding steps of different conversion methods are different , It is proved that the decoding error probability can reach 0 when the code length is long enough. In the simulation experiment of BPSK signal in AWGN channel, At 0.5 bit rate, the proposed method improves 1.1dB over Polar code when FER is 0.001, and 1.4dB over Polar code when FER is 0.0001.Â

As Metaverse emerges as the next-generation Internet paradigm, the ability to efficiently generate content is paramount. AIGenerated Content (AIGC) emerges as a key solution, yet the resourceintensive nature of large Generative AI (GAI) models presents challenges. To address this issue, we introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models in wireless edge networks to ensure broad AIGC services accessibility for Metaverse users. Nonetheless, an important aspect of providing personalized user experiences requires carefully selecting AIGC Service Providers (ASPs) capable of effectively executing user tasks, which is complicated by environmental uncertainty and variability. Addressing this gap in current research, we introduce the AI-Generated Optimal Decision (AGOD) algorithm, a diffusion model-based approach for generating the optimal ASP selection decisions. Integrating AGOD with Deep Reinforcement Learning (DRL), we develop the Deep Diffusion Soft Actor-Critic (D2SAC) algorithm, enhancing the efficiency and effectiveness of ASP selection. Our comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL algorithms. Furthermore, the proposed AGOD algorithm has the potential for extension to various optimization problems in wireless networks, positioning it as a promising approach for future research on AIGC-driven services. The implementation of our proposed method is available at: https://github.com/Lizonghang/AGOD.

We explore essential factors pertaining to the spatial directivity of quantum radiating source systems (QSSs), encompassing quantum antennas and quantum sensors. Our primary focus is on their capacity to control the emission of photons in specific spatial directions. We present a comprehensive definition of quantum directivity, drawing inspiration from Glauberâ\euro™s photon detection theory. This definition closely parallels the framework of analogous concepts in classical antenna theory. By conducting thorough conceptual and mathematical analysis, we address the challenge of characterizing the directive properties of a general QSS. Essentially, our approach presents a computational model that relies solely on the radiation field operatorâ\euro™s density as input.

This work addresses symbol detection in timevarying Massive Multiple-Input Multiple-Output (M-MIMO) systems. While conventional symbol detection techniques often exhibit subpar performance or impose significant computational burdens in such systems, learning-based methods have shown potential in stationary scenarios but often struggle to adapt to nonstationary conditions. To address these challenges, we introduce a hierarchy of extensions to the Learned Conjugate Gradient Network (LcgNet) M-MIMO detector. Firstly, we present Preconditioned LcgNet (PrLcgNet), which incorporates a preconditioner during training to enhance the uplink M-MIMO detectorâ\euro™s filter matrix. This enhancement enables the detector to achieve faster convergence with fewer layers compared to the original, nonpreconditioned approach. Secondly, we introduce an extension of PrLcgNet, known as the Dynamic Conjugate Gradient Network (DyCoGNet), specifically designed for time-varying environments. DyCoGNet leverages self-supervised learning with forward error correction, enabling autonomous adaptation without the need for explicit labeled data during training. It also employs metalearning, facilitating rapid adaptation to unforeseen channel conditions. Our simulation results demonstrate that PrLcgNet achieves faster convergence, lower residual error, and comparable symbol error rate (SER) performance to LcgNet in stationary scenarios. Furthermore, in the time-varying context, DyCoGNet exhibits swift and efficient adaptation, achieving significant SER performance gains compared to baseline cases without metalearning and online self-supervised learning.

This paper describes the historical trajectory of video conferencing systems, spanning from their earlier mechanical and analog origins in the 1920s to the sophisticated IP-based services delivered from the cloud in the 2020s. Each technological age is examined, highlighting the technical and functional aspects that characterized its evolution. Commercial landmarks of each age are presented providing a comprehensive overview of the most prominent offerings at pivotal moments in the timeline. By examining the past and speculating on the future, this paper aims to provide a holistic understanding of the development, current state, and forthcoming trends in video conferencing technology.