EAI Endorsed Transactions on Mobile Communications and Applications
EAI Endorsed Transactions on Mobile Communications and Applications is an open-access, a peer-reviewed scholarly journal focused on all aspects of mobile communications theories, technologies, systems, and applications. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a triannual frequency. Authors are not charged for article submission and processing. INDEXING: CrossRef, Google Scholar, ProQuest, EBSCO, CNKI, Dimensions eISSN: 2032-9504
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Secure and Robust AI-Driven Beamforming for Terahertz (THz) 6G Networks: A Federated Learning Approach
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.
ultra-reliable low-latency communication (URLLC)
Copyright (c) 2025 Milad Rahmati https://creativecommons.org/licenses/by-nc-sa/4.0
5G NR Uplink Performance Optimization: A Comprehensive Study on PRACH and PUSCH Interference Management
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).
Copyright (c) 2025 Désiré Guel, P. Justin Kouraogo, Flavien Hervé Somda, Boureima Zerbo, Oumarou Sié https://creativecommons.org/licenses/by-nc-sa/4.0
Mitigating Intermittent Connectivity Problems in Vehicle-to-Vehicle Communication (V2VC): A Sparse Network Computational Model (SNCM)
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.
Copyright (c) 2023 EAI Endorsed Transactions on Mobile Communications and Applications https://creativecommons.org/licenses/by/3.0/
Enhancing Mental Health With PHILOI: A Comprehensive Analysis of Mood Music and Chatbot Module
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.
Centralized multicasting AODV routing protocol optimized for intermittent cognitive radio ad hoc networks
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. To better understand the routing in this network scenario, this paper presents two analytic models for the AODV and MAODV protocols based on queuing theory. The analytic models simulate unicast and multicast AODV in terms of factors such as queuing delay, throughput, and network scalability. Numerical analysis shows that MAODV outperforms AODV. Furthermore, the suggested routing protocols' performance was tested using network simulations utilizing the following metrics: throughput, Routing Path delay, Node Relay delay, and Spectrum Mobility delay. The simulation findings suggest that the MAODV protocol outperforms the AODV protocol.
Improving 6G Network Spectrum Efficiency with Non-Cooperative and Cooperative Spectrum Sharing Using NOMA and Massive-MIMO
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.
Cooperative Cognitive Radio Network (CCRN)
Un-Cooperative Cognitive Radio Network (Un-CCRN)
massive multiple-input and multiple-output (M-MIMO)
Evaluation of accuracy for prediction of soft tissue profile changes in non-growing patients undergoing orthodontic treatment using cephalometric android application.
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.
Webceph cephalometric software, soft tissue prediction, VTO prediction, class I malocclusion, class II malocclusion
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.
Li-Fi Technology Based Long Range Free-Space Optics Data Transmit System Evaluation
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.
ns-3 Simulation Based Exploration of LTE Handover Optimization
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.
Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning
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.
Intelligent Reflecting Surface Aided Secure Communication with Federated Learning
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.
Deep Model Training and Deployment in Heterogeneous IoT Networks
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.
Formation control of multiple unmanned vehicles based on graph theory: A Comprehensive Review
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.
Copyright (c) 2022 EAI Endorsed Transactions on Mobile Communications and Applications https://creativecommons.org/licenses/by/3.0/
Deep Non-Orthogonal Multiple Access Network Assisted by Intelligent Reflecting Surface
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.
Collaborative Relay Radio Network Using Reconfigurable Intelligent Surface
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.
A Comparative Analysis of the various Power Allocation Algorithm in NOMA-MIMO Network Using DNN and DLS Algorithm
The high data rate, huge spectrum efficiency, successive interference cancellation (SIC), and ultra-reliable low latency (URLL) are of the demand for next-generation technologies. Non-orthogonal multiple access (NOMA) scheme provides multi-user scaling (multiplexing), optimum spectral efficiency (SE), excellent user-pairing improvement, and a single resource block sharing by multiple users because of which it is a more preferable scheme over orthogonal multiple access (OMA) for the next generation technologies. This article investigates comparative analysis of various power allocation algorithms in multiple-input multiple-output-NOMA (MIMO-NOMA) technology and to come up with the best power allocation algorithm which suited best for MIMO-NOMA technology. Firstly, comparison analysis will be carried out considering direct methodologies followed by power allocation algorithm using Deep Neural Network (DNN) along with the Depth limited search (DLS) algorithm. These techniques are tested on two users initially then followed by multi-user communication. Allocating optimal power to the poor signal strength user terminal (user not receiving appropriate signal power) is a difficult task in actual scenario, and moreover, SIC also creates complexity in the proper allocation of Base station (BS) source power. The above problems can be solved with the assistance of the DNN along with the DLS algorithm, where the weaker user receives maximum power and the stronger user receives minimum power. The DNN-MIMO-NOMA technology, which is based on the DLS algorithm, helps user terminals to get their signals free from noise (inter_user_interference) and with greater precision. The DLS process (algorithm) offers higher potential in MIMO-NOMA with DNN technology for successfully applying SIC. Here, MIMO helps to improve the channel gain. A DLS provides an optimum power allocation (OPA) based on the position of user equipment. The simulation results show that the power allocation method using DNN along with DLS algorithm achieves better performance than the traditional multi-user.
Successive interference cancellation (SIC)
Influence of Internet of things on human psychology (internet of thoughts) for education, healthcare, and businesses
INTRODUCTION: The study investigated how technological innovations and Embedded Systems, processes, and Communication tools influence human psychology. The IoTs have succeeded to connect humans with things, and it is now preoccupied with understanding how humans react to these things by influencing our actions, reactions, and behavior. OBJECTIVES: To identify items that IoTs use to influence human psychology in education, healthcare, and business and to find out how IoTs utilize AI, ML, Cloud computing, NLP, ICT tools, and big data technology to overpower human psychology. METHODS: The methods “Behavior-oriented drive and influential function method” and the qualitative-quantitative method were used. The qualitative-quantitative method makes use of 3 research questions while behavior-oriented drive and influential function focus on statistical data. RESULTS: The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function and above 90% positive respondents for the qualitative-quantitative method. CONCLUSION: The study concluded that the high dependency of humans on IoTs services is the reason for the IoTs influence on our thoughts, actions, and behavior.
Energy-efficient and high-spectrum-efficiency wireless transmission
With the deployment and commercialization of the fifth-generation (5G) mobile communication networks, the access nodes and data volume of wireless networks are showing massive and explosive growth. In this paper, we firstly review the research progress on the energy-efficient and spectrum-efficient wireless transmission, from the perspectives of both latency and energy consumption. We then provide several solutions to high-efficiency wireless transmission for the 5G wireless networks. Finally, we give some discussions about the future road for the design of energy-efficient and spectrum-efficient wireless transmission, which provides some theoretical reference for the system design of wireless networks and Internet of Things (IoT) networks.
The Intelligent Distributed Data Storage for Wireless Communications in B5G Networks
With the deployment and commercialization of the fifth-generation (5G) mobile communication network, the access nodes and data volume of wireless network show a massive and blowout growth trend. Taking beyond 5G (B5G) edge intelligent network as the research object, based on the deep integration of storage / computing and communication, this paper focuses on the theory and key technology of system intelligent transmission, so as to effectively support the related applications of B5G edge intelligent network in the future. This paper analyzes the research status of data storage, studies the real field distributed storage computing system, and designs the corresponding flashback shift code and error correction scheme with low storage space overhead.
Millimeter Wave Cellular Communication Performances and Challenges: A Survey
The demand for high data rates, combined with the exponential growth of mobile data trafficking, and has prompted the use of millimeter-wave (mm-wave) spectrum for 5G mobile communication. So, for constructive assessment, this study employed various research publications, institutional reports, and other materials given at the conference. This survey article investigates the features of the mm-wave propagation channel and highlights the main challenges, solutions, and benefits associated with their utilization, as well as an analysis of their performance. The researchers observed that by reducing the difficulties and significant losses with various strategies, mm-wave cellular communication may extend up to 200m in a single cell and densification is essential for vast area coverage. The article also examines the right technical implementation approach, as well as the economic benefits and existing and predicted market situation of mm-wave cellular communication from the operator’s perspective. The outcome will be useful information for any operator or stakeholder in mm-wave communication.
A Churn Prediction System for Telecommunication Company Using Random Forest and Convolution Neural Network Algorithms
INTRODUCTION: Customer churn is a severe problem of migrating from one service provider to another. Due to the direct influence on the company's sales, companies are attempting to promote strategies to identify the churn of prospective consumers. Hence it is necessary to examine issues that influence customer churn to yield effective solutions to minimize churn. OBJECTIVES: The major purpose of this work is to create a model of churn prediction that assists telecom operatives to envisage clients that are more probably to be prone to churn. METHODS: The experimental strategy for this study leverages the machine learning techniques on the telecom churn dataset, employing an improved Relief-F feature selection algorithm to extract related features from the enormous dataset. RESULTS: The result demonstrates that CNN has a high prediction capability of 94 percent compared to the 91 percent Random Forest classifier. CONCLUSION: The results are of enormous relevance to the telecommunication business in improving churners and loyal clients.
Distributed Joint Channel Assignment and Power Control for Sum Rate Maximization of D2D-Enabled Massive MIMO System
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.
An efficient geo-routing aware MAC protocol for underwater acoustic networks
In this paper, we propose an efficient geo-routing aware MAC protocol (GOAL) for underwater acoustic networks. It smoothly integrates self-adaptation based REQ/REP handshake, geographic cyber carrier sensing, and implicit ACK to perform combined channel reservation and next-hop selection. As a result, it incorporates the advantages of both a geo-routing protocol and a reservation-based medium access control (MAC) protocol. Specifically, with its self-adaptation based REQ/REP, nodes can dynamically detect the best next-hop with low route discovery cost. In addition, through geographic cyber carrier sensing, a node can map its neighbors’ time slots for sending/receiving DATA packets to its own time line, which allows the collision among data packets to be greatly reduced. With these features, GOAL outperforms geo-routing protocols coupling with broadcast MAC. Simulation results show that GOAL provides much higher end-to-end reliability with lower energy consumptions than existing Vector-Based Forwarding (VBF) routing with use of a broadcast MAC protocol. Moreover, we develop a theoretical model for the probability of a successful handshake, which coincides well with the simulation results.
Copyright (c) 2022 EAI Endorsed Transactions on Mobile Communications and Applications
A decentralized scheduling algorithm for time synchronized channel hopping
Time Synchronized Channel Hopping (TSCH) is an existing Medium Access Control scheme which enables robust communication through channel hopping and high data rates through synchronization. It is based on a time-slotted architecture, and its correct functioning depends on a schedule which is typically computed by a central node. This paper presents, to our knowledge, the first scheduling algorithm for TSCH networks which both is distributed and which copes with mobile nodes. Two variations on scheduling algorithms are presented. Aloha-based scheduling allocates one channel for broadcasting advertisements for new neighbors. Reservation- based scheduling augments Aloha-based scheduling with a dedicated timeslot for targeted advertisements based on gossip information. A mobile ad hoc motorized sensor network with frequent connectivity changes is studied, and the performance of the two proposed algorithms is assessed. This performance analysis uses both simulation results and the results of a field deployment of floating wireless sensors in an estuarial canal environment. Reservation-based scheduling performs significantly better than Aloha-based scheduling, suggesting that the improved network reactivity is worth the increased algorithmic complexity and resource consumption.
Reliability-based server selection for heterogeneous VANETs
Heterogeneous wireless networks are capable of providing customers with better services while service providers can offer more applications to more customers with lower costs. To provide services, some applications rely on existing servers in the network. In a vehicular ad-hoc network (VANET) some mobile nodes may function as servers. Due to high mobility of nodes and short lifetime of links, server-to-client and server-to-server communications become challenging. In this paper we propose to enhance the performance of server selection by taking link reliability into consideration in the server selection mechanism, thereby avoiding extra client-to-server hand-offs and reducing the need of server-to-server synchronization. As a case study we focus on location management service in a heterogeneous VANET. We provide a routing algorithm for transactions between location servers and mobile nodes. We assume that location servers are vehicles equipped with at least one long- range and one short-range radio interfaces, whereas regular nodes (clients) are only equipped with a short-range radio interface. The primary goal of our design is to minimize hand-offs between location servers while limiting the delays of location updates. Taking advantage of vehicle mobility patterns, we propose a mobility-aware server selection scheme and show that it can reduce the number of hand-offs and yet avoid large delays during location updates. We present simulation results to show that proposed scheme significantly lowers the costs of signaling and rate of server hand-offs by increasing the connection lifetimes between clients and servers.
Analytical modeling of address allocation protocols in wireless ad hoc networks
Detailed descriptions of Internet Protocol Address Assignment (IPAA) and Mobile Ad Hoc Network Configuration (MANETconf) are presented and state diagrams for their behavior are constructed. Formulae for the expected latency and communication overhead of the IPAA protocol are derived, with the results being given as functions of the number of nodes in the network with message loss rate, contention window size, coverage ratio, and the counter threshold as parameters. Simulation is used to validate the analytical results and also to compare performance of the two protocols. The results show that the latency and communication overhead for MANETconf are significantly higher than the measures of the IPAA protocol. Results of extensive sensitivity analyses for the IPAA protocol are also presented.
The dawn of the 21st century has seen a growing interest in vehicular networking and its myriad potential applications. The initial view of practitioners and researchers was that radio-equipped vehicles could keep the drivers informed about potential safety risks and increase their awareness of road conditions. The view then expanded to include access to the Internet and associated services. This position paper proposes and promotes a novel and more comprehensive vision namely, that advances in vehicular networks, embedded devices and cloud computing will enable the formation of autonomous clouds of vehicular computing, communication, sensing, power and physical resources. Hence, we coin the term, autonomous vehicular clouds (AVCs). A key feature distinguishing AVCs from conventional cloud computing is that mobile AVC resources can be pooled dynamically to serve authorized users and to enable autonomy in real-time service sharing and management on terrestrial, aerial, or aquatic pathways or theaters of operations. In addition to general-purpose AVCs, we also envision the emergence of specialized AVCs such as mobile analytics laboratories. Furthermore, we envision that the integration of AVCs with ubiquitous smart infrastructures including intelligent transportation systems, smart cities and smart electric power grids will have an enormous societal impact enabling ubiquitous utility cyber-physical services at the right place, right time and with right-sized resources.
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A Trace-Driven Analysis of Wireless Group Communication Mechanisms
Wireless access is increasingly ubiquitous while mobile devices that use them are resource rich. These trends allow wireless users to collaborate with each other. We investigate various group communication paradigms that underly collaboration applications. We synthesize durations when members collaborate using wireless device availability traces. Wireless users operate from a variety of locations. Hence, we analyzed the behavior of wireless users in universities, corporations, conference venues, and city-wide hotspots. We show that the availability durations are longer in corporations followed by university and then in hotspots. The number of simultaneously available wireless users is small in all the scenarios. The session lengths are becoming smaller while the durations between sessions are becoming larger. We observed user churn in all the scenarios. We show that synchronous mechanisms require less effort to maintain update synchronicity among the group members. However, distributed mechanisms require a large number of replicas in order to propagate updates among the users. For asynchronous mechanisms, we show that pull-based mechanisms naturally randomize the times when updates are propagated and thus achieve better performance than push based mechanisms.We develop an adaptive approach that customizes the update frequency using the last session duration and show that this mechanism exhibits good performance when the required update frequency intervals are large. We also show that for a given number of gossips, it is preferable to propagate updates to all available nodes rather than increasing the frequency while correspondingly reducing the number of nodes to propagate updates.We develop a middleware to illustrate the practicality of our approach.