The rapid evolution of wireless communication has driven the need for sixth-generation (6G) networks, which aim to deliver unprecedented data rates, ultra-low latency, and seamless connectivity. Terahertz (THz) frequencies are a cornerstone of 6G technology due to their vast spectrum availability, but they introduce new challenges such as severe path loss, atmospheric attenuation, and security vulnerabilities. To overcome these issues, AI-driven beamforming has gained attention as a powerful solution for optimizing signal transmission and interference mitigation. However, existing AI-based methods remain susceptible to adversarial attacks, privacy breaches, and suboptimal adaptation in dynamic environments [1]. This paper introduces a federated learning (FL)-based AI-driven beamforming approach tailored for THz-enabled 6G networks. The framework ensures privacy-preserving intelligence by training beamforming models collaboratively across distributed edge devices, eliminating the need for centralized data sharing. To enhance security, we integrate adversarial defense techniques, strengthening resilience against potential attacks that could degrade beamforming accuracy. Through extensive simulations, we evaluate key performance metrics, including beamforming efficiency, spectral efficiency, signal-to-noise ratio (SNR), and resistance to adversarial perturbations. Our results indicate that the proposed FL-based beamforming approach improves adaptability, mitigates security threats, and enhances overall network performance compared to traditional centralized AI models. This study provides a scalable and secure AI-driven solution for 6G beamforming, paving the way for reliable and privacy-aware THz communications. Future work will explore real-world deployment and the integration of quantum-secure encryption techniques to further fortify security in 6G networks.
The evolution of 5G New Radio (NR) technology offers unprecedented speeds, ultra-low latency, and the capability to connect billions of devices. However, these advancements come with significant challenges, particularly in managing interference during uplink communication. This study presents a comprehensive investigation into the optimization of 5G NR uplink performance by focusing on two critical channels: the Physical Uplink Shared Channel (PUSCH) and the Physical Random Access Channel (PRACH). The research explores the impact of intra-cell and inter-cell interference on these channels, highlighting how various User Equipment (UE) and cell configuration parameters influence performance. Key Performance Indicators (KPIs) such as Block Error Rate (BLER) and Correct Detection Rate (CDR) are utilized to assess the effectiveness of proposed interference management strategies. Through rigorous simulations and empirical evaluations, the study provides valuable insights into optimizing 5G NR networks, aiming to enhance the robustness and reliability of uplink communication in diverse interference scenarios. The findings underscore the importance of adaptive resource allocation and interference mitigation techniques in achieving superior network performance and quality of service (QoS).
INTRODUCTION: An accurate prediction in soft tissue changes is of great importance for orthodontic treatment planning. Patients find it difficult to imagine how their facial appearance may change after orthodontic treatment without a visual reference. Predicting the postoperative facial appearance may thus be useful for managing expectations, easing communication, and researching different treatment choices. Computer-assisted programs are still relatively expensive and are not portable in comparison to smartphones, and the accuracy of soft tissue profile prediction of these android applications has not been thoroughly assessed. The purpose of the study is to assess how well the Webceph cephalometric Android application predicts changes in soft tissue profile following orthodontic treatment.MATERIALS AND METHOD: A total of 50 patients were screened for eligibility, and 24 young adult patients (8 males, 16 females; mean age 24.8 ±3.9 years) were finally included in the study based on the inclusion and exclusion criteria. The landmarks and parameters of the Legan and Burstone soft tissue analysis were used for the cephalometric analyses. The cephalometric tracings of the actual treatment result and the Webceph predicted treatment outcome was superimposed to calculate the prediction errors. Paired t-test used to compare the statistical differences between the predicted and actual treatment outcomes of the parameters used in the legan and burstone soft tissue analysis.RESULTS: There were significant differences between the predicted and actual values in parameters of legan and burstone soft tissue analysis (P\0.05). It was reported that the prediction in two parameters (i.e., Lower face throat (Sn-Gn-C angle) (Cm-sn-ls) Nasolabial angle) was a significant difference from the actual modifications in class I bimaxillary protrusion group and there were substantial changes in the prediction of two characteristics (facial convexity (G-Sn-Pg angle) and inter labial (Stms-Stmi) in the class II group.CONCLUSIONS: The Webceph VTO prediction in soft tissue changes after the orthodontic treatment in patients with bimaxillary protrusion and class II malocclusion is the most accurate for the nasolabial angle and the least accurate for the mandibular prognathism parameter.
INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.
The project aim was to develop an app that would enable the recording and monitoring of behaviour related to specific aspects of wellness, as well as support those aspects of wellness that are entertainment-related. Our main goal was to envision and develop an app with the well-being of users in mind. People’s moods can be improved upon or changed by music, with music and mental health tightly intertwined. Music is frequently used to complement or change an individual’s mood. While there are advantages to mood-appropriate music, it may cause us to remain in a depressed, angry, or nervous state. A survey was conducted to examine these aspects. After performing a lot of research and interviews in this area, we found 68% of those surveyed listen to music according to their mood or to change their mood. This inspired us to build an application that not only plays music but also recommends songs to users, eliminating the daily nuisance of selecting the right music, which can waste valuable time. As mental balance is an essential component of healthy existence in today's hectic world, to enhance the practicality of our app, as icing on the cake, we included an AI chatbot that not only converses with the user but also provides them with suitable advice on their concerns.
The advancement of wireless technology is affected by Spectrum scarcity and the overcrowding of free spectrum. Cognitive Radio Ad Hoc Networks (CRAHNs) have emerged as a possible solution to both the scarcity and overcrowding challenges of the spectrum. The CRAHNs ensure that the Secondary Users (SUs) do co-exist with Primary Users (PUs) in a non-interfering manner. The SUs access the licensed spectrum opportunistically when they are idle. CRAHNs have many use cases which include intermittent networks here referred to as intermittent CRAHNs (ICRAHNs). For example, the Military (MCRAHNs). MCRAHN is complex and characterized by a dynamic topology which is subject to frequent partitioning and route breakages due to attacks and destruction in combat. This study optimizes the routing protocols for intermittent networks such as the MCRAHNs. ICRAHN routing is a challenge due to the network’s intermittent attribute, which is subject to destruction in the case of MCRAHN which is characterized by frequent link breakages. The performance of the proposed routing scheme was evaluated through network simulations using the following metrics: throughput, and Routing Path delay, Node Relay delay, Spectrum Mobility delay. The simulation results show that the MAODV is the best-performing algorithm.
Applying federated learning into the covert communication can not only ensure the communication reliability, but also reduce the probability of enemy detection. The use of airspace resources is an effective way to achieve covert communication. However, most of the existing works on the airspace covert communication represented by MIMO need to adapt to the channel state and cannot improve the channel, resulting in the performance bottleneck of covert communication. The intelligent reflective surface (IRS) provides a new perspective for the covert communication by flexibly adjusting the reflection phase shift of the incident signal and intelligently configuring the wireless channel. However, the potential of IRS in the covert communication is far from being fully exploited. To solve this issue, this paper firstly performs a comprehensive literature review on the secure communication with the aid of federated learning, and then gives some challenges on the secure communication in poor channel state. In further, this paper provides some solutions to the challenges on the secure communication, where some results are provided to show the advantages. The research results have important theoretical and practical significance for forming a new research paradigm of airspace intelligent and controllable covert communication and promoting the application and popularization of covert communication in various fields of security.
There is an expectation that standards for sixth-generation (6G) wireless communication networks in the future would give previously unheard-of speeds for the flow of information as well as spectrum optimization. This will present new issues for 6G networks. Non-orthogonal multiple access (NOMA) is one of the most efficient ways to boost the spectrum efficiency (SE) of a 6G network. The most promising contemporary technologies, such as cognitive radio (CR) and multiple access, can be used to improve SE. When NOMA's network-oriented multi-access capabilities are combined with those of the Cognitive Radio Network (CRN), a new era of efficient communication is expected to dawn. To improve the spectral efficiency (SE) of the NOMA DL power domain (PD), this work presents two distinctive strategies that are used in conjunction with un-cooperative and cooperative CRN (Un-CCRN and CCRN) in the event that one primary user (PU) is unable to receive through the dedicated channel due to interference or noise. Users' distances, power placement coefficients, and transmit powers (TPs) vary across the proposed three network topologies, and over the proposed three network sizes of 128x128, 256x256, and 512x512 Massive Multiple Input Multiple Output (M-MIMO). Performance is analyzed while simultaneously considering channel instability and successive interference cancellation (SIC). The channels of fading are modelled after frequency-dependent Rayleigh fading. MATLAB is used to determine the proposed model's SE. With 128x128, 256x256, and 512x512 M-MIMO integrated into the DL NOMA system, the system's SE performance is improved by 73%, 82%, and 87%, respectively; with the Un-CCRN NOMA model, the improvement is 75%, 83%, and 88%; and with the CCRN-NOMA model, the improvement is 75.8%, 84%, and 88.3%. The SE is significantly improved by employing M-MIMO technology. The acquired expressions agree with the outcomes of the provided Monte Carlo simulations, providing further evidence for the validity of our investigation.
Network simulator (ns-3) is a reputed simulation platform for performance evaluation of cellular networks. In this work, we explore the use of ns-3 for tracking of successful handovers (HO) and handover failures and consequent impact on 4G LTE network throughput with the aim of discovering new analytical relations about HOs and new methods to optimize the resulting throughput. Decreased cell sizes in newer generation networks lead to increasing number of handovers and handover failures that have significant impact. We begin by reviewing analytical models in the literature that aim to predict number of HO and HO failures in terms of HO control and network parameters. We initially conduct a suite of exhaustive validation studies of such analytical models, based on the simulation execution manager (SEM) for ns-3 for parallelization. Via this, we discover new causal relations relating HO failures and choice of HO control parameters on network throughput. Based on these initial results, we next evaluate the application of Gaussian process regression for prediction of instantaneous network throughput and bandit algorithms as an effective mechanism to optimize throughput over time. The new relations discovered help better understand the impact of input handover control parameters on the number of handovers and handover failures allowing us to fine tune them. The new optimization and prediction methods discovered give good gains over baseline algorithms and help accurately predict throughput respectively.
INTRODUCTION: The most flexible and reliable technological system is Wi-Fi, which is made possible by a wireless connection that transmits data using radio frequencies. Wi-Fi networks, however, encounter numerous issues related to power supply, availability, efficiency, and security as a result of the various access points. While relational waves describe the medical device, Wi-Fi radios produce radio waves that are very dangerous for patients. This document offers line-of-sight communication between the transmitter and receiver using LED technology. Li-Fi technology is a method that transmits audio data using LED light, which is faster and more efficient than Wi-Fi. Since it is practically ubiquitous, light can be used for communication as well. A cutting-edge technology called optical communication includes a subset called light fidelity. By sending out visible light, the Li-Fi device enables wireless intranet communication. This paper is an in-depth study and analysis of Light Fidelity (Li-Fi), a novel technology that transmits data at high speeds over a wide spectrum by using light as a medium of transmission. The research fields that are pertinent to Li-Fi networks are thoroughly analyzed and categorized in this paper.OBJECTIVES: High speed data transmission, receive, share, broadcast through light in free space optical communication system by Li-Fi technology.METHODS: We followed some methods and developed a unique method to develop this project. which is VLC, OOK, a Lambertian discharge mechanism, LOS, NLOS, or a CMOS optical receiver.RESULTS: Successfully, we transmitted and received audio, video, and other data, which is very high-rated and near the 2 GB/s range.CONCLUSION: Visible light communication may be a fast-evolving technique in the field of wireless technology. Li-Fi is a wireless data transfer system that is both fast and inexpensive. It can also be used in potentially hazardous situations, such as in nuclear power plants, without causing electromagnetic interference. We are considering adding more features to our project in the future to get better outcomes.
The Fifth Generation (5G) Network will bring different types of services, namely: EMBB, mMTC and URLLC and as more devices connect to the network, each user device request for data capacity will continue to grow. The increase in the number of devices and capacity request for each device will require an increase in network capacity, which will also need an increase in the number of Base Stations in the network. More Base Stations will increase the the Mobile Network Operator’s capital investment and operation costs. However, this increase in CAPEX and OPEX will not provide a corresponding increase in ARPU as users tend to be less willing to pay more as their capacity request increases. Mobile vendors and Mobile Network Operators will face the challenge of providing higher capacity for the same or less ARPU for their customers to maintain their customer base and maintain the business profitability. C-RAN was identified as a new and promising paradigm to help Mobile Network Operators reduce their CAPEX and OPEX while delivering higher capacity to their customers and maintaining business profitability. How C-RAN resources are allocated and managed within the 5G network will determine how efficient and profitable this optimisation process will be. In this article, we provide a high-level review of the crucial enabling 5G Technologies and an exhaustive review of C-RAN resource allocation algorithms for 5G networks with emphasis on resource allocation metrics/parameters. The main resource allocation metrics considered in this work include BBU computational/processing resource, capacity, Power/ energy consumption, wavelength, UE-RRH mapping and RRH-BBU mapping. Furthermore, Energy Harvesting in 5G C-RAN is discussed, including its architecture, categorised taxonomy and requirements. Lastly, future research directions and open research issues for efficient C-RAN resource allocation are highlighted.
Device-to-device (D2D) communications underlaid massive multiple-input multiple-output (MIMO) systems have been recognized as a promising candidate technology to achieve the challenging fifth-generation (5G) network requirements. This integration enhances network throughput, improves spectral efficiency, and offloads the traffic load of base stations. However, the co/cross-tier interferences between cellular and D2D communications caused by resource sharing is a significant challenge, especially when dense D2D users exist in an underlay mode. In this paper, we jointly optimize the channel assignment and power allocation to maximize the sum data rate while maintaining the interference constraints of cellular links. Due to the lack of network-wide information in large scale networks, resource management and interference coordination is hard to be implemented in a centralized way. Therefore, we propose a three-stage stable and distributed resource allocation and interference management scheme based on local information and requires little coordination and communication between devices. We model the channel allocation optimization problem in the first stage as a many-to-one matching game. In the second stage, the algorithm adopts a cost charging policy to solve each user’s power control problem as a non-cooperative game. In the third stage, the algorithm search for swap blocking pairs until stable matching exist. It is shown in this paper that the proposed algorithm converges to a stable matching and terminates after finite iterations. Simulation results show that the proposed algorithm can achieve more than 86% of the average transmission rate performance of the optimal matching with lower complexity.
The intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) symbiotic communication technology is expected to enhance the access performance, energy efficiency and spectrum efficiency of the communication network, and is regarded as an important candidate technology to support the evolution of the sixth-generation (6G) towards large-scale, high-capacity and sustainable development. However, the relevant research of this technology is still at the initial stage, and many key challenges have not been fully studied. Therefore, it is urgent to open up relevant research ideas and methods to promote its development and early implementation, so as to make it an effective 6G technology. In view of this, this paper intends to carry out the research on the theory and method of IRS assisted NOMA symbiotic transmission, starting from the analysis of the active and passive symbiotic mechanism of NOMA transmission protocol. Based on this, we further study the efficient symbiotic modulation transmission technology and multi-dimensional resource optimization allocation method. The research content of this paper is to explore the transmission theory and technology of high energy efficiency and high frequency spectrum efficiency for 6G, and break through the bottleneck problem of spectrum and energy consumption encountered by wireless communication, which has important practical significance for the wireless communication.
In this paper, we have studied a model of a relay radio network system using Reconfigurable Intelligent Surface (RIS). Specifically, we used a relay network that uses RIS when there is an extra direct link from the Source (S) to the Destination (D). Next, an approximate closed-form expressions of the Outage Probability (OP) and Ergodic Capacity (EC) are considered. Based on the simulation results of OP and EC, the results show that our proposed system is more optimal than the system using supported RIS without direct link and the system without using RIS. In addition, changing the number of the RIS reflecting elements and the RIS’s location near (S) or (D) has a significant impact on the performance of the system. The analytical expression match the simulation results through the Monte Carlo simulation method. Furthermore, the simulation results of energy efficiency (EE) also show that when the target spectral efficiency (SE), Rth, is high (more than 5.45 b/s/Hz), the system using supported RIS with direct link will help reduce the transmit power and optimize the most energy compared to the other two systems.
As a typical form of machines learning, deep learning has attracted much attention from researchers. It can independently construct (train) basic rules according to the sample data in the learning process. Especially in the field of machine vision, neural networks are usually trained by supervised learning, that is, by example data and predefined results of example data. In this paper, we firstly overview the current research progress on the deep model training and deployment on the heterogeneous Internet of Things (IoT) networks, by taking into account both the latency and energy consumption from various devices in the system. We then summarize the existing challenges on the model training and model deployment on the heterogeneous IoT devices. We further give some feasible solutions to solve the challenges on the model training and model deployment on the heterogeneous IoT devices. The study in this paper can serve as an important reference for the development of deep model training and model deployment for heterogeneous IoT networks.
In recent years, formation control for multiple unmanned vehicles becomes an active research topic that has received a lot of attention from scientists due to its superior advantages compared with other conventional systems. Algebraic graph and graph rigidity theories are the two main mathematical backgrounds of the formation control theory. The graph theory is used to describe the interconnections among vehicles in formation while rigid graph theory - an important subset of graph theory - ensured that the inter-vehicle distance constraints of the desired formation are enforced via the graph rigidity. This paper provides a comprehensive review of graph theory supporting formation control for groups of unmanned aerial vehicles (UAV) or swarm UAVs. The background of the theory and the recent developments of graph-theory-based formation control are reviewed. We provide a cohesive overview of the formation control and coordination of multiple vehicles. Finally, some challenges and future potential directions in formation control are discussed.
Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.
Device to Device (D2D) communication has emerged as the most promising paradigm for optimizing spectral and energy efficiency, reliability as well as increasing network throughput in the emerging cellular generation.D2D resource optimization scheme has been thoroughly investigated in the literature to manage interference and enable its smooth integration into the future cellular network in recent years as an increasing number of papers are published each year.We believe systematic categorization of literature in the area will help readers to comprehend the strengths, weaknesses, and trends of the solution approach.However, a survey that discusses all aspects, requirements, and challenges of D2D resource optimization is largely missing.Hence, in this paper, we aim to develop a comprehensive survey that fills the gaps found in the previous literature.Based on the surveyed papers, it will figure out ideas that have been thoroughly explored and those that assume the potential for further research.
In this work, we describe an approach for tangible interaction for smartphone-based VR using visual markers.Smartphone-based VR represents a low barrier to entry given that many people nowadays own a smartphone device and that VR headsets for these devices are affordable and quick to set up when compared to desktopbased VR.Tangible interaction in smartphone-based VR has not been much explored, despite the fact that it can result in easy to use and engaging experiences.We explore a marker-based solution to object tracking that allows tangibles to be created in an easy and cheap way, maintaining the overall system accessible.We describe several types of interactions that can be created with visual markers and we present three prototypes of the Tangible VR Book: a tangible with a book form-factor that we designed for architectural heritage exploration.We also present user feedback on their expectations regarding the interaction with the Tangible VR Book.
