HPC (high-performance computing) plays a more important role now because of AI training needs for big models. The latest HPC requirement is to scale up to fifty thousand processors (or servers). HPC is based on the direct memory exchange between processors that is typically called â\euroœinterconnectâ\euro?. Historically, it was a big scientific challenge to define a topology for interconnect due to the low scalability of switching elements. Initially, on-board switching ASICs supported below 10 directions/buses which creates a challenge to scale even to hundreds of processors. Different topologies invented at that time (like Torus) have many hops for the traffic that greatly increase cost and latency but decrease reliability. Moreover, many compromises resulted in the requirement for applications to be engineered in such a way as to distribute traffic unequally inside and between local groups. Later, Infiniband external switches greatly improved the scalability to dozens of interfaces permitting to creation of much more scalable topologies (like Dragonfly) with less number of hops. Infiniband considerably improved latency and cost. Yet, the scale of Inifiband still pushes for restrictions (like performance) that are far from optimal. The latest generation of Ethernet switches achieved the scale (hundreds of interfaces) that is enough to create topologies with minimal compromise or even without any compromise at all: the minimal number of hops that results in minimal cost and latency, maximum reliability, wire-speed or close to it (regulated at design time), no need for network load distribution at the application layer. The scale without any compromise could satisfy the biggest AI training requirements. The scale with minimal compromises could be 8 times high then needed now.

 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

Accurate node localization holds prime significance across a range of future network applications. In this letter, we present a novel scheme for instantaneous localization of Internet-of-Remote-Things (IoRT) terminals from the satellite/network side, employing a single-channel probing by a single-antenna-equipped single-orbiter. Our method measures both Round Trip Time (RTT) and Doppler shift, which aid the proposed localization scheme. A geometric framework to establish a probabilistic relationship between satellite transmit (Tx) and receive (Rx) positions with the target IoRT terminal location is proposed. The key steps of the proposed framework are summarized in an algorithmic form. Furthermore, a thorough performance analysis is also conducted where the impact of various channel and geometric parameters on the localization accuracy is investigated.Â

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.

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.

For wideband transceivers operating at millimeter wave and terahertz frequencies, the implementation of conventional digital predistortion for nonlinearity mitigation faces significant challenges due to the limited availability and/or complexity of high-speed digital signal processing. In this paper, a simple received signal model is proposed for wideband system with nonlinearity and other practical impairments, such as transmitter (Tx) and receiver (Rx) I/Q imbalances (IQIs), carrier frequency offset (CFO), and phase noise, to enable low-complexity impairment mitigation. An expanded memory polynomial (EMP) model is firstly proposed to capture Tx IQI and the nonlinearity over the entire transceiver chain. Exploiting the CFO and a novel transmission protocol, a blind Rx IQI estimation is also proposed. The noise enhancement after Rx IQI and CFO compensation is then evaluated as a noise factor related to the mean-square-error of the Rx IQI estimation. As a result, the received signal of the wideband system is finally modelled as an EMP plus additive noises followed by a band-limited noisy receiver filter. Simulation results using a millimeter wave system with 2.5 GHz bandwidth and 73.5 GHz carrier frequency are presented to verify the accuracy of the EMP modelling and validate the theoretical analyses.

Many companies and a growing number of governments now have guides, frameworks, or principles claiming to govern their use of AI. Seven years ago, virtually none did. This article presents original research on documents produced by 193 countries and the top 100 companies by market capitalization. A key shift occurred in 2016 when the Cambridge Analytica scandal showed the potential harms of misused AI. The widespread use of large language models such as ChatGPT beginning in late 2022 is further increasing calls for governance of the AI space. Analyzing the evolving practice of releasing such documents and the language that they use offers important insights into how norms around AI are spreading and changing â\euro” and where they might go next.

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.

This letter proposes and analyzes three different methods to achieve robust consensus control in a leaderâ\euro“follower multiagent system framework prone to bounded disturbance in finite time. In the first method, a sliding mode surface is proposed using the basic definition of consensus. An upper bound on noise is considered while designing the surface. Then a Lyapunov stability analysis is used to determine the control law. Second method deals with using a high-gain disturbance observer to dynamically estimate the noise. A sliding mode surface was then developed using the estimates. Further, a Lyapunov analysis was done to show the stability of the system. Lastly, a new sliding surface based on High Gain DIsturbance Observer is proposed which alleviates the problem of mismatched uncertainties. A Lyapunov analysis was then performed to ensure convergence of the system in finite time. The robustness of the proposed approach is validated through simulations.

Edge/Fog computing recently emerged as a novel distributed virtualized computing paradigm, where cloud services are extended to the edge of the network, thereby increasing network capacity and reducing latencies. In fog computing, applications are composed of {\it microservices} that are mapped to edge computing and communication devices ({\it fog nodes}). A crucial component in fog computing is placement algorithms that assign microservices to fog nodes since they determine the overall system performance in terms of energy consumption, communication costs, load balancing, and others. Placement strategies devised for cloud computing are generally centralized and, therefore, not well suited for decentralized fog systems. In this paper, we consider the joint optimization of two conflicting objectives, energy consumption at fog nodes and communication costs of applications, as a game between fog nodes and applications where each agent is modeled to control one objective. We follow a Markov approximation method for the design of a fully distributed autonomic service placement strategy without central coordination or global state information. Evaluation results show that the new approach provides a more optimal solution as compared to previous autonomic placement algorithms.

Federated Learning (FL) has emerged as a decentralized machine learning paradigm that facilitates collaborative training of a global model (GM) across multiple devices while maintaining data privacy. Traditional FL systems suffer from centralized validation of local models and GM updates, compromising transparency and security. In this paper, we propose DAO-FL, a smart contract-based framework that leverages the power of Decentralized Autonomous Organizations (DAOs) to address these challenges. DAO-FL introduces the concept of DAO Membership Tokens (DAOMTs) as a governance tool within a DAO. DAOMTs play a crucial role within the DAO, facilitating members’ enrollment and expulsion. Our framework incorporates a Validation-DAO for decentralized input verification of the FL process, ensuring reliable and transparent validation of local model uploads. Additionally, DAO-FL employs a multi-signatures approach facilitated by an Orchestrator-DAO to achieve partially decentralized GM updates, and thus decentralized output verification of the FL process. We present a comprehensive system architecture, detailed execution workflow, implementation specifications, and qualitative evaluation for DAO-FL. Evaluation under threat models highlights DAO-FL’s out-performance against traditional-FL (FedAvg), effectively countering input and output attacks. DAO-FL excels in scenarios where decentralized input and output verification are crucial, offering enhanced transparency and trust. In conclusion, DAO-FL provides a compelling solution for FL,  reinforcing the integrity of the FL ecosystem through decentralized decision-making and validation mechanisms.Â

The development of our neoteric scientific fields has reached such a magnificent level that the benefits from innovation have extended to all regions, including those most distant. Modern information and communication network systems have greatly improved the overall effectiveness and performance of the wireless networks. With the emergence of cellular networks, the wireless communication network system has evolved from the first generation to the sixth generation (6G), which will provide an integrated framework for a variety of utilities and applications. The goal of 6G network systems is to improve channel capacity, low bit rate error, efficient signal transmission, and low latency while providing reliable connectivity and seamless communication. Various technologies are integrated with 6G network systems to provide controlled, efficient, and reliable communication. One emerging technology for 6G communication network systems is reconfigurable intelligent surfaces (RIS), which is ideal for smooth and controllable wireless signal transmission. RIS uses smart beamforming methods with the assistance of an electronic circuit controller to provide superior signal quality. This study presents a complete overview of RIS, including its hardware architecture, key characteristics, control mechanism, operating frequency, communication duplex mode, and operating mode. The study also elaborates on the RIS operational environment, advantages of implementing RIS, deployment, and different types of communication by utilizing RIS. Finally, we conclude with a discussion of challenges and future research directions to overcome limitations to achieve the goal of RIS implementation and RIS-aided communication for 6G communication systems.

We propose a complete system to enable progressive coding with quality scalability of the mesh geometry, in MPEGâ\euro™s state-of-the-art Video-based Dynamic Mesh Coding (V-DMC) framework. In particular, we propose an alternative method for encoding the subdivision wavelet coefficients in V-DMC, using a mesh-based zerotree coding approach. The proposed method works directly in the native 3D mesh space. It allows us to identify parent-child relationships amongst the wavelet coefficients across different subdivision levels, which can be used to achieve an efficient and versatile coding mechanism. We demonstrate that, given a starting base mesh, a target subdivision surface and a desired maximum number of zerotree passes, our system produces an elegant and visually attractive lossy-to-lossless mesh geometry reconstruction with no further user intervention. Moreover, lossless coefficient encoding with our approach is shown to require almost the same bitrate as the default displacement coding methods in V-DMC. Yet, our approach provides several levels of quality resolution within each target bitrate, while the current solutions encode a single quality level only. To the best of our knowledge, this is the first time that a zerotree-based method has been proposed and demonstrated to work for the compression of dynamic time-varying meshes, and the first time that an embedded quality-scalable approach has been used in the V-DMC framework.

Industry 4.0, also known as the Fourth Industrial Revolution, is characterized by the incorporation of advanced manufacturing technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and automation. With the increasing adoption of Industry 4.0 technologies, it becomes crucial to implement effective security measures to safeguard these systems from cyber attacks. The development of intrusion detection systems (IDS) that can detect and respond to cyber threats in real-time is crucial for securing Industry 4.0 systems. This research topic seeks to investigate the various techniques and methodologies employed in developing IDS for Industry 4.0 systems, with a particular concentration on identifying the most effective solutions for protecting these systems from cyber attacks. In this study, we compared supervised and unsupervised intrusion detection algorithms. We utilized data collected from heterogeneous sources, including Telemetry datasets of IoT and The industrial Internet of things (IIoT) sensors, Operating systems (OS) datasets of Windows 7 and 10, as well as Ubuntu 14 and 18 TLS and Network traffic datasets simulated by the School of Engineering and Information Technology (SEIT), UNSW Canberra @ the Australian Defence Force Academy (ADFA). The preliminary results of IDS accuracy are extremely encouraging on the selected data for this study (Windows OS and Ubuntu OS), which motivates the continuance of this line of inquiry using a variety of other data sources to formulate a general recommendation of IDS for Industry 4.0.

This paper develops a systematic strategy to construct a model of an IP Network with multiple weight links and proposes a multi-objective distributed routing algorithm (MODRA) for the One-to-All Multi-objective Shortest Path (MOSP) Problem. The formal proof of a loop-free routing in distributed mode is given, as well as extensive experiments are performed in simulated networks to show the algorithm performance. The proposed algorithm is based on computation of complete and exact Pareto-optimal set of paths for each destination and specific routing path selection with the minimal multi-dimension path length. In this work, the proposed MODRA is tested on four network topologies with two-weight links and different configurations, and the performance is evaluated w.r.t. multiple upper-bound path constraints. The algorithm is used to compute a Routing Information Base (RIB) table in each node. Then the distributed hop-by-hop packet routing is simulated and the actual path traversed by a packet is compared to the initially computed one. Our approach supports arbitrary topology, number of additive link weights and shows optimal performance in terms of computing feasible paths satisfying the given multiple upper-bound constraints. The proposed algorithm is implemented and tested in a simulated environment, and the framework of this work could be adopted to design other routing algorithms for multi-objective distributed routing in IP Networks. The algorithm performance evaluation shows its' ability to compute optimal paths w.r.t. multiple upper-bound constraints, guarantee loop-free distributed routing and satisfy strict execution time requirements. The algorithm is fully compatible with the current router architecture and can be easily implemented in a router.

Molecular communication via diffusion (MCvD) expects Brownian motions of the information molecules to transmit information. However, the signal propagation largely depends on the geometric characteristics of the assumed flow model, i.e., the characteristics of the environment, design, and position of the transmitter and receiver, respectively. These characteristics are assumed to be lucid in many ways by either consideration of one-dimensional diffusion, unbounded environment, or constant drift. In reality, diffusion often occurs in blood-vessel-like channels. To this aim, we try to study the effect of the biological environment on channel performance. The Red-Blood Cells (RBCs) found in blood vessels enforces a higher concentration of molecules towards the vessel walls, leading to better reception. Therefore, in this paper we derive an analytical expression of Channel Impulse Response (CIR) for a dispersion-advection-based regime, contemplating the influence of RBCs in the model and considering a point source transmitter and a realistic design of the receiver.

This technical note presents proof of the convergence of the Alternating Direction Method of Multipliers (ADMM) for addressing the sharing problem when applied in conjunction with two algorithms: 1) the stochastic STEP-GP algorithm and 2) its variant named LGP, which includes adaptive uniform quantization. For the case using LGP, the coordinator can assign different quantization resolutions at each iteration and we assume that the number of bits that can be assigned is unrestricted and can go to infinity. This document describes and analyzes the two methods for integrating learning and uniform quantization into the ADMM to reduce its communication overhead and a general formulation of their communication decision method. The problems are formulated for a multi-agent setting.

This paper presents a novel, power- and hardware-efficient, multiuser, multibeam RIS (Reflective Intelligent Surface) architecture for multiuser MIMO, especially suited to operate in very high frequency bands (e.g., high mmWave and sub-THz), where channels are typically sparse in the beamspace and line-of-sight (LOS) is the dominant component. The key module is formed by an active multiantenna feeder (AMAF) with a small number of active antennas, placed in the near field of a RIS with a much larger number of passive controllable reflecting elements. We propose a pragmatic approach to obtain a steerable beam with high gain and very low sidelobes. Then K independently controlled beams can be achieved by closely stacking K such AMAF-RIS modules. Our analysis includes the mutual interference between the modules and the fact that, due to the delay difference of propagation through the AMAF-RIS structure, the resulting channel matrix is frequency selective even in the presence of pure LOS propagation. We consider a 3D geometry and show that “beam focusing” is in fact possible (and much more effective in terms of coverage) also in the far-field, by creating spotbeams with limited footprint both in angle and in range. Our results show that: 1)  simple RF beamforming without computationally expensive baseband digital multiuser precoding is sufficient to practically eliminate multiuser interference when the users are chosen with sufficient angular/range separation, thanks to the extremely low sidelobes of the proposed module; 2) the impact of beam pointing errors with standard deviation as large as 2.5 deg and RIS quantized phase-shifters with quantization bits > 2 is essentially negligible; 3) The proposed architecture is more power efficient and much simpler from a hardware implementation viewpoint than standard RF beamforming active arrays with the same beamforming performance. As a side result, we show also that the array gain of the proposed AMAF-RIS structure grows linearly with the RIS aperture, in line with classical results for standard reflector antennas.

Virtualized Network Function (VNF) sharing among multiple Fifth Generation (5G) slices allows network operators to increase the efficiency and utilization of the network. However, this sharing of VNFs can result in a degradation of the performance of the slices in the presence of an attack. In this paper, we evaluate the impact of flooding attacks on the performance of 5G slices with different VNF sharing configurations. We consider two VNF sharing configurations, in the first configuration, the Session Management Function (SMF) and User Plane Function (UPF) are shared among the deployed slices, while the SMF and UPF of the slices are isolated in the second configuration. The performance of these configurations is evaluated using different traffic types under two flooding attack scenarios; a ping flood attack targeting the data plane of 5G network, and a registration request flood attack directed at the control plane of 5G network. Our results showed different responses in the control and data planes. In the data plane, isolating VNFs of the slices provides better performance and mitigates the adverse effects of the attacks studied.

Time-Sensitive Networking (TSN) is designed to provide deterministic performance for real-time systems, which usually have hard delay requirements for Time-Triggered (TT) traffic. In order to guarantee deterministic delays, TSN scheduling assigns specific time slots to TT frames for their transmissions by TSN switches on their paths. However, several factors may lead to scheduling violations, meaning that TT frames are not sent as scheduled. Scheduling violations can cause the increment of delays experienced by TT frames, which may breach their delay requirements. Existing TSN scheduling methods cannot deal with scheduling violations. In our research, we propose TSN-Violation Mitigation (TSN-VM), which dynamically reconfigures the schedule at each TSN switch distributedly on a periodic basis, to mitigate the occurrence of scheduling violations. TSN-VM is built upon our new real-time detection mechanism to discover local violation information in each port of TSN switches. TSN-VM is the first real-time algorithm that can deal with scheduling violations in TSN. Its convergence conditions are demonstrated in the paper. Using TSN-VM, we can reduce the number of lag violations by more than 90.74$\%$ and decrease the average and median delays by more than 95.57$\%$ in the simulated avionic system and industrial Ethernet compared with systems without TSN-VM.

With advancements in distributed autonomous systems (e.g., vehicles, sensors, and robots) in the 5G/6G era, sidelink communication technology has evolved as a distributed communication system in the third generation partnership project (3GPP). However, the current low-rate point-to-point sidelink communication design is not suitable for rapid development of such autonomous systems. Instead, based on sidelink, developing distributed wireless personal area networks (WPANs) with a drastically higher rate are essential. The overarching goal of this study is to explore the possibility of sidelink communication evolution to 1) form a distributed and autonomous WPAN and 2) support millimeter wave (mmWave) bands. Our core idea is to merge several design concepts of the precedented mmWave WPAN standards, i.e., IEEE 802.15.3c/11ad, into the sidelink communications, thereby bridging the gap between the two separated systems. This paper presents the anatomy of the IEEE 802.15.3c/11ad system with a focus on the formation of mmWave WPANs among distributed nodes and their operation. In addition, the current status of sidelink communication system design is highlighted, along with the missing building blocks, which are required to develop 3GPP sidelink-based mmWave WPAN systems. Simulation results on network formation signaling in the mmWave WPANs shed light on merging IEEE 802.15.3c/11ad concepts into 3GPP sidelink communication. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.Â

The purpose of this paper is to offer a comprehensive survey on the major technical issues and current research trends in  supporting VR services over wireless networks. Additionally, the paper explores the fundamentals of VR technologies,  applications, QoE requirements, spectrum requirements and key enabling technologies. Finally, a comprehensive discussion regarding potential research directions aimed at enhancing wireless VR experiences is also presented. Thus, this survey paper is structured to provide a strong baseline for the researchers working in wireless VR systems.

In the evolving landscape of the IT industry, the dichotomy between Work-From-Office (WFH) and Return-To-Office (RTO) models has become a focal point, especially in IT Consulting and Software Development. This study embarks on a comparative analysis of these models in the United States and Germany, delving into their impacts on productivity, commuting, stress, and the economic costs associated with maintaining office spaces. The research is grounded in a comprehensive literature review that explores the historical and current work models in IT, assesses productivity metrics, analyzes commuting patterns, and evaluates the influence of work-related stress on IT professionals. Additionally, it considers the economic implications of office spaces, a factor critical to organizational decision-making. Employing a case study methodology, the research scrutinizes an IT consulting firm in the US and a software development company in Germany. These case studies are instrumental in examining the multifaceted aspects of WFH and RTO models. The productivity analysis is conducted through quantitative measures like project completion rates and qualitative assessments from employee feedback. Commuting impacts are evaluated in terms of time, cost, environmental footprint, and employee satisfaction. Stress levels are measured through well-being surveys and turnover rates, providing insights into the psychological impacts of different work models. A pivotal aspect of the study is the economic analysis of office costs, encompassing real estate expenses, utilities, maintenance, and the potential savings from remote work models. The comparative analysis aims to draw parallels and contrasts between the US and German contexts, highlighting how cultural and economic factors shape the adoption and effectiveness of WFH and RTO models. It also explores the environmental considerations of commuting in the IT sector and the complex interplay between work models, stress, job satisfaction, and economic efficiency. The discussion section extrapolates the broader implications of these findings, offering policy recommendations for IT companies navigating the post-pandemic work environment. It addresses the challenges of balancing productivity with employee well-being and the economic realities of office maintenance. In conclusion, the study synthesizes the findings from the case studies, advocating for a flexible, economically viable approach that harmonizes productivity with employee well-being and company costs. It underscores the necessity for IT companies to adopt adaptable work models that consider not only the productivity and well-being of employees but also the economic realities of office maintenance. The study also highlights the need for ongoing research to understand the long-term economic impacts of WFH and RTO models on the IT industry.

Quantum approximate optimization algorithm (QAOA) is used for NP-hard problems on noisy intermediate-scale quantum (NISQ) devices. We demonstrate QAOA’s near-optimum maximum likelihood (ML) decoding for short block-lengths in Gaussian channels using random linear codes and channel coded modulation. Simulations with a p-layer QAOA decoder for  p ∈ [1, 4], coding rates R = k/n ∈ [0.3, 1], signal-to-noise ratio (SNR)  ∈  [0, 10] dB and k ∈ [10, 26] show near-optimum bit and block error rates. We conjecture near-optimum performance for p ∈ [1, 10], R = 0.5, SNR = 10 dB and k ≦ 250 indicating QAOA’s potential in short block-length decoding.