Efficient Communication Research Articles

Examining the efficiency of D2D communication employing DF-enabled multiple relay systems amid co-channel interference

Device-to-device (D2D) communication is emerging as a potential paradigm in contemporary wireless communication systems. Enabling D2D communication in the mmWave range has numerous challenges that must be overcome. The biggest concern is the introduction of interference from several sources. This study investigates the impact of co-channel interference (CCI) on D2D communication when energy harvesting (EH) and decode-and-forward (DF) techniques are used across several relay nodes. It includes mathematical equations for the cumulative distribution function (CDF) of the Signal-to-Interference-Plus-Noise ratio (SINR), as well as formulas for calculating the network's outage probability (OP), throughput (TP), and availability rate (AR). Furthermore, it produces an asymptotic expression for analyzing the probability density function (PDF) of the instantaneous SINR, providing a simple and complete approach to understanding its OP, TP, and AR properties. The analytical results are supported by Monte Carlo simulations and MATLAB implementation.

DCO-FBMC-based VLC NOMA with SIC cancellation: enhancing multi-user communication efficiency

DCO-FBMC-based VLC NOMA with SIC cancellation: enhancing multi-user communication efficiency

Secure Dynamic Scheduling for Federated Learning in Underwater Wireless IoT Networks

Federated learning (FL) is a distributed machine learning approach that can enable Internet of Things (IoT) edge devices to collaboratively learn a machine learning model without explicitly sharing local data in order to achieve data clustering, prediction, and classification in networks. In previous works, some online multi-armed bandit (MAB)-based FL frameworks were proposed to enable dynamic client scheduling for improving the efficiency of FL in underwater wireless IoT networks. However, the security of online dynamic scheduling, which is especially essential for underwater wireless IoT, is increasingly being questioned. In this work, we study secure dynamic scheduling for FL frameworks that can protect against malicious clients in underwater FL-assisted wireless IoT networks. Specifically, in order to jointly optimize the communication efficiency and security of FL, we employ MAB-based methods and propose upper-confidence-bound-based smart contracts (UCB-SCs) and upper-confidence-bound-based smart contracts with a security prediction model (UCB-SCPs) to address the optimal scheduling scheme over time-varying underwater channels. Then, we give the upper bounds of the expected performance regret of the UCB-SC policy and the UCB-SCP policy; these upper bounds imply that the regret of the two proposed policies grows logarithmically over communication rounds under certain conditions. Our experiment shows that the proposed UCB-SC and UCB-SCP approaches significantly improve the efficiency and security of FL frameworks in underwater wireless IoT networks.

A novel method for reducing the effect of doppler frequency shift in high-speed railway mobile communication using machine learning

Currently, high-speed railway systems are rapidly expanding worldwide, necessitating reliable and efficient mobile communication solutions. However, the Doppler frequency shift caused by the high speeds of trains presents significant challenges to communication systems, particularly those using OFDM in LTE. This paper presents a novel for Doppler frequency compensation in high-speed railway communication based on the results of estimating the train's velocity using machine learning algorithms. By leveraging advanced algorithm such as neural networks, our method dynamically predicts and compensates for Doppler shifts in real-time. In this proposed novel, the Doppler frequency value is calculated based on the actual train’s velocity and the scenario of a high-speed railway, then the Doppler frequency is used to compensate the carrier frequency offset (CFO) directly at the Access Point (AP) device on the train. We conduct simulations to evaluate the effectiveness of our proposed solution in a high-speed railway scenario. The results demonstrate a marked improvement in communication reliability and data integrity, highlighting the potential of machine learning to enhance the performance of mobile communication systems in high-speed railways. The results of the proposed model are evaluated based on the system’s bit error rate BER after the CFO compensation decreases and the ratio of average energy per bit (Eb) to noise power density (N0) (Eb/N0) increases. This shows that the proposed solution works effectively and reduces the system’s bit errors while improving the communication performance. This study opens new avenues for integrating intelligent systems in transportation networks, ensuring seamless connectivity, and improving passenger experience

Early-stage entrepreneurs’ communicative strategies in managing knowledge ambiguity

ABSTRACT Prior research demonstrates the importance of knowledge seeking on startup performance, but the process of accessing knowledge is less studied. Knowledge ambiguity in entrepreneurship is embedded in the complexities and interconnectedness of knowledge sources, knowledge components, knowledge transfer mechanisms, and knowledge adaptation processes. This study explores the communicative strategies entrepreneurs use to cope with knowledge ambiguity, specifically with the use of online and offline channels. Interview data were collected from 20 early-stage entrepreneurs in the knowledge-intensive industries in the New York metropolitan area. Five strategies emerged from the data: manage public visibility, optimize knowledge relevance, enhance communication efficiency, expand knowledge awareness, and access indirect knowledge. This research contributes to the knowledge of how communication and media use unfold in resource-constrained environments and to the growing literature of entrepreneurial bricolage (make do with what is at hand) in knowledge management.

Uncertainty-Aware Federated Reinforcement Learning for Optimizing Accuracy and Energy in Heterogeneous Industrial IoT

The Internet of Things (IoT) technology has revolutionized various industries by allowing data collection, analysis, and decision-making in real time through interconnected devices. However, challenges arise in implementing Federated Learning (FL) in heterogeneous industrial IoT environments, such as maintaining model accuracy with non-Independent and Identically Distributed (non-IID) datasets and straggler IoT devices, ensuring computation and communication efficiency, and addressing weight aggregation issues. In this study, we propose an Uncertainty-Aware Federated Reinforcement Learning (UA-FedRL) method that dynamically selects epochs of individual clients to effectively manage heterogeneous industrial IoT devices and improve accuracy, computation, and communication efficiency. Additionally, we introduce the Predictive Weighted Average Aggregation (PWA) method to tackle weight aggregation issues in heterogeneous industrial IoT scenarios by adjusting the weights of individual models based on their quality. The UA-FedRL addresses the inherent complexities and challenges of implementing FL in heterogeneous industrial IoT environments. Extensive simulations in complex IoT environments demonstrate the superior performance of UA-FedRL on both MNIST and CIFAR-10 datasets compared to other existing approaches in terms of accuracy, communication efficiency, and computation efficiency. The UA-FedRL algorithm attain an accuracy of 96.83% on the MNIST dataset and 62.75% on the CIFAR-10 dataset, despite the presence of 90% straggler IoT devices, attesting to its robust performance and adaptability in different datasets.

In-Depth Assessment of the Influence of Leadership Styles on Team Performance through the Lens of Wisdom Leadership

Examining team efficacy with the lens of leadership styles' effects on team performance of Wisdom Leadership (WL) highlights how different leadership styles impact team performance and highlights the part that WL plays in directing and improving team results through careful, well-considered decision-making. WL can be difficult to assess and use consistently in a variety of circumstances due to its subjective character, and it can ignore environmental considerations and practical limits that impact team effectiveness. There are 195 participants’ data are collected in leadership styles’ impact on team performance through the WL lens was gathered at random. Some questionnaires are also randomly gathered. Decision-making effectiveness (DME), conflict resolution skills (CRS), emotional intelligence (EI), visionary thinking (VT), team cohesion (TC), and employee satisfaction (ES) these factors are used to determine with the lens of WL, how leadership styles affect team performance. The statistical of PLS-SEM, Cronbach's Alpha, discriminate validity tests, Average Variance Extracted (AVE), and Exploratory Factor Analysis (EFA) to evaluate leadership styles' impact on team performance, ensuring reliability and theoretical alignment. The results validate all of the hypotheses and demonstrate that variables such as DME, CRS, EI, VT, TC, and ES significantly positively correlate with WL.When examining how team performance is affected by leadership styles through the perspective of WL. The WL approaches provides a supportive and moral atmosphere that greatly improves team performance. The significance of incorporating wisdom into leadership techniques is underscored by the way that type of leadership fosters trust, efficient communication, and sustained success.

UAV swarm path planning approach based on integration of multi-population strategy and adaptive evolutionary optimizer

Abstract Addressing the optimal path planning problem encountered by swarm of Unmanned Aerial Vehicle (UAV) in three-dimensional space under multiple constraints, the Multi-population Adaptive Cuckoo Search and Grey Wolf Optimizer (MACSGWO) integrates Multi-Population (MP) strategies and adaptive evolutionary optimizer including the enhanced Adaptive Grey Wolf Optimizer (AGWO) and Adaptive Cuckoo Search (ACS).
The optimizer strategically divides the initial population into multiple sub-groups, enabling each sub-group to independently iterate. 
During the iteration process, the algorithm adaptively adjusts parameters based on the optimal fitness values obtained by each sub-group after each iteration. The iteration cycle is divided into two stages: during the global exploration phase, each sub-group autonomously executes AGWO and periodically shares the fitness information of the Alpha wolf with other sub-groups, accelerating convergence.
In the subsequent local optimization phase, MACSGWO dynamically decides whether to initiate ACS based on the disparity in the best fitness of each sub-group after each iteration, assisting the algorithm in escaping local optima. 
In experiments involving various complex benchmark functions and swarm path planning scenarios, MACSGWO demonstrated significant superiority in solution stability, convergence speed, and optimal convergence value compared to multiple existing variant algorithms. The integration of MACSGWO with the best relay UAV selection strategy further optimized the communication efficiency within the swarm. MACSGWO ensures the efficient resolution of UAV swarm path planning problems, providing robust support for optimization challenges in complex, multi-constraint scenarios.

ACFL: Communication-Efficient adversarial contrastive federated learning for medical image segmentation

Federated learning is a popular machine learning paradigm that achieves decentralized model training on distributed devices, ensuring data decentralization, privacy protection, and enhanced overall learning effectiveness. However, the non-independence and identically distributed (i.e., non-IID) nature of medical data across different institutes has remained a significant challenge in federated learning. Current research has mainly focused on addressing label distribution skew and classification scenarios, overlooking the feature distribution skew settings and more challenging semantic segmentation scenarios. In this paper, we present communication-efficient Adversarial Contrastive Federated Learning (ACFL) for the prevalent feature distribution skew scenarios in medical semantic segmentation. The core idea of the approach is to enhance model generalization by learning each client’s domain-invariant features through adversarial training. Specifically, we introduce a global discriminator that, through contrastive learning in the server, trains to differentiate feature representations from various clients. Meanwhile, the clients learn common domain-invariant features through prototype contrastive learning and global discriminator training. Furthermore, by utilizing Gaussian mixture models for virtual feature sampling on the server, compared to transmitting raw features, the ACFL method possesses the additional advantages of efficient communication and privacy protection. Extensive experiments on two medical semantic segmentation datasets and extension on three classification datasets validated the superiority of the proposed method.

Edge Computing in Healthcare: Innovations, Opportunities, and Challenges

Edge computing promising a vision of processing data close to its generation point, reducing latency and bandwidth usage compared with traditional cloud computing architectures, has attracted significant attention lately. The integration of edge computing in modern systems takes advantage of Internet of Things (IoT) devices and can potentially improve the systems’ performance, scalability, privacy, and security with applications in different domains. In the healthcare domain, modern IoT devices can nowadays be used to gather vital parameters and information that can be fed to edge Artificial Intelligence (AI) techniques able to offer precious insights and support to healthcare professionals. However, issues regarding data privacy and security, AI optimization, and computational offloading at the edge pose challenges to the adoption of edge AI. This paper aims to explore the current state of the art of edge AI in healthcare by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology and analyzing more than 70 Web of Science articles. We have defined the relevant research questions, clear inclusion and exclusion criteria, and classified the research works in three main directions: privacy and security, AI-based optimization methods, and edge offloading techniques. The findings highlight the many advantages of integrating edge computing in a wide range of healthcare use cases requiring data privacy and security, near real-time decision-making, and efficient communication links, with the potential to transform future healthcare services and eHealth applications. However, further research is needed to enforce new security-preserving methods and for better orchestrating and coordinating the load in distributed and decentralized scenarios.

Enhancing Transmission Efficiency in Non-IID Federated Learning by Integrating Quantization into an Existing Trustworthiness Model

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data, thus preserving privacy. In this approach, data is not transmitted between the client and the base station. Instead, a model is sent to each device, where it is trained locally, and then retransmitted back to the base station. Each iteration of this process is known as a communication round. However, FL faces challenges, especially when data is not independently and identically distributed (Non-IID). Non-IID data means that the data across clients can vary significantly in distribution, leading to situations where certain classes or features are overrepresented in some clients and underrepresented in others. This lack of uniformity quickly leads to biased model updates and reduced performance. To address this, previous studies have introduced trustworthiness metrics to ensure more reliable model aggregation, minimizing accuracy losses associated with Non-IID data. Another significant challenge is the high transmission load, as model updates between clients and the base station are resource-intensive. Model parameters are transmitted between clients and base stations, which can strain communication channels and slow down the entire process, especially when dealing with larger models and datasets. This communication overhead is a bottleneck that limits the scalability of FL, particularly in resource-constrained environments. Our research addresses these challenges by integrating quantization into the trustworthiness model specifically in a Non-IID scenario. Quantization reduces the precision of model parameters to minimize data transmission, resulting in more efficient communication. We applied different levels of quantization intensity to a model trained on the CIFAR-10 dataset and found that certain methods can significantly reduce transmission overhead without substantial accuracy loss. Our findings suggest that combining quantization with trustworthiness metrics can significantly enhance the efficiency and potentially improve the adoption of Federated Learning in resource-constrained environments.

Energy and throughput aware adequate routing for wireless sensor networks using integrated game theory method

A Wireless Sensor Network (WSN) is usually made up of a large number of discrete sensor nodes, each of which requires restricted resources, including memory, computing power, and energy. To extend the network lifetime, these limited resources must be used effectively. In WSN, clustering constitutes one of the best methods for optimizing network longevity and energy conservation. In this work, we proposed a novel Energy and Throughput Aware Adaptive Routing (ETAAR) algorithm based on Cooperative Game Theory (CGT). To achieve the energy efficient and improved data rate routing in WSN, we are applied two game theories of CGT and coalition game. The main part of this routing mechanism is cluster head selection and clustering the nodes to perform energy efficient and throughput effective communication between the nodes. In first stage, CGT based utility function which adopts both energy and throughput is utilized to handpick the CH nodes. In the second stage, along with the energy and throughput, average end-to-end delay is considered for the adaptive time slot transmission to avoid collision in the coalition game approach. MATLAB tool is used for simulation. The simulation results shows that the proposed ETAAR protocol is outperforms than earlier works of routing in terms of residual energy, PDR, energy due ratio, average end-to-end delay, dead nodes. The network lifetime of 48% extension, energy saving of 60% and 52.5% of delay shortage attained in ETAAR.

Enhancing Detection Frequency and Reducing Noise Through Continuous Structures via Release-Controlled Transfer Toward Light-Based Wireless Communication.

Organic photodetectors (OPDs) have received considerable attention owing to their superior absorption coefficient and tunable bandgap. The introduction of bulk-heterojunction (BHJ) structure aims to maximize charge generation, however, its response speed is constrained by the random distribution of donor and acceptor. Herein, a multiple-active layer design consisting of a single acceptor layer and a bulk-heterojunction layer (A/BHJ structure) is introduced, which combines the benefits of both the planar junction and the BHJ, improving photo-sensing. A transfer process is employed for this structure, which involves calculating the energy release rate at each interface, considering temperature and velocity. Consequently, the OPD with the A/BHJ structure is successfully fabricated through transfer printing, resulting in reduced dark current, superior detectivity (1.06 × 1013 Jones), and rapid response, achieved by creating a high hole injection barrier and suppressing trap sites within the interfaces. By thoroughly investigating charge dynamics in the structure, the A/BHJ structure-based OPD attains large bandwidth detection with high signal-to-noise. An efficient wireless data communication system with digital-to-analog conversion is showcased using the A/BHJ structure-based OPD.

A Machine Learning Approach for Path Loss Prediction Using Combination of Regression and Classification Models.

One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as 7.3 dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 MHz, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)-LoRa, etc.

Efficient federated learning for distributed neuroimaging data.

Recent advancements in neuroimaging have led to greater data sharing among the scientific community. However, institutions frequently maintain control over their data, citing concerns related to research culture, privacy, and accountability. This creates a demand for innovative tools capable of analyzing amalgamated datasets without the need to transfer actual data between entities. To address this challenge, we propose a decentralized sparse federated learning (FL) strategy. This approach emphasizes local training of sparse models to facilitate efficient communication within such frameworks. By capitalizing on model sparsity and selectively sharing parameters between client sites during the training phase, our method significantly lowers communication overheads. This advantage becomes increasingly pronounced when dealing with larger models and accommodating the diverse resource capabilities of various sites. We demonstrate the effectiveness of our approach through the application to the Adolescent Brain Cognitive Development (ABCD) dataset.

Critical Results in Laboratory Medicine.

Timely and accurate notification of critical results is crucial in laboratory medicine and mandated by accreditation standards like ISO15189. Alert lists do, however, vary widely and clinical laboratories typically rely on a combination of in-house agreed and/or literature-based critical values. Communication by phone is still the preferred method of notification, but digital communication could help improve communication of critical results. We review the available evidence concerning critical result thresholds and critical result notification practices. The evidence is ranked using an adaptation of the Stockholm Hierarchy. In addition, we propose an evidence-based list of critical result thresholds for hospitalized patients that laboratories can use as a starter list and further customize based on the clinical needs of their patient population. A clear distinction between critical results and significantly abnormal results is essential for effective and timely healthcare interventions. Implementation of a policy using differentiated thresholds taking into account individual patient characteristics and how fast medical attention is needed, and the use alternative communication methods could enhance communication efficiency and reduce notification fatigue.

Smart Transfer Planer with Multiple Antenna Arrays to Enhance Low Earth Orbit Satellite Communication Ground Links

In this study, we propose a smart transfer planer equipped with multiple antenna arrays to improve ground links for low Earth orbit (LEO) satellite communication. The STP features a symmetrical structure and is strategically placed on both ends of a window, serving both indoor and outdoor environments. Using the window glass as a medium, energy transmission occurs through a coupling mechanism between the planers. The design focuses on large array antenna design, beamforming networks, and coupler design on both sides of the glass. Beamforming networks enable the indoor and outdoor antenna arrays to switch beams in various directions, optimizing high-gain antennas with narrow beamwidths. Through electromagnetic induction and filter couplers, a robust signal transmission channel is established between indoor and outdoor environments. This setup significantly enhances communication efficiency, particularly in non-line-of-sight environments.

Enhanced channel estimation with atomic norm minimization and reconfigurable intelligent surfaces in mmWave MIMO systems

SummaryThe performance of millimeter‐wave (mmWave) multiple‐input multiple‐output (MIMO) systems has been significantly enhanced by the incorporation of dynamic reconfigurable intelligent surfaces (RIS). This paper proposes a novel dynamic channel estimation technique that combines dynamic atomic norm minimization with dynamic RIS to optimize RIS‐aided mmWave MIMO systems. Leveraging the dynamic nature of both atomic norm minimization and RIS, the proposed approach efficiently adapts to changing environmental conditions, providing robust and accurate channel estimation. By dynamically optimizing the RIS configuration, the system achieves improved spectral and energy efficiency, enabling high‐speed and reliable communication in challenging mmWave environments. Theoretical analysis and simulation results demonstrate the effectiveness of the proposed dynamic channel estimation technique, highlighting its potential for enhancing the performance of future wireless communication systems.

Unlocking the impact of international financial support to infrastructure, energy efficiency, and ICT on CO2 emissions in India

Financial resource constraints prevent developing nations from investing in the infrastructure needed for the green transition. For this reason, it is well known that numerous financial aid programs are offered globally, mainly to wealthy developing nations whose economies are predicated on high energy consumption. It hasn't been looked into if these resources are allocated to worthwhile projects. This study examines how foreign financial assistance in this area affects India's infrastructure emissions. The effects of natural resources, economic growth, information and communication technology, energy efficiency (oil and gas), and natural resources on the environment are being investigated as well. The novel multivariate quantile on-quantile regression technique is used in this context to examine a time-series data set spanning 2000–2021. According to the findings, there is a correlation between reduced carbon emissions and increased foreign financial aid. Likewise, advancements in energy efficiency and information and communication technologies also benefit the ecosystem. However, India's emissions are rising due to its abundant natural resources and economic expansion. Policy recommendations were made regarding the need for India to use international financial aid primarily to strengthen environmentally friendly infrastructure rather than solving problems such as current account deficit or budget deficit.

Plug-and-play four-state modulation continuous-variable quantum key distribution

In this paper, a novel and highly efficient secure communication scheme is proposed through the integration of four-state modulation quantum key distribution and plug-and-play technology. This scheme serves the purpose of transmitting information reliably while preserving data confidentiality between two communicating parties. The most important advantage of the proposed continuous-variable QKD system is that the birefringence effect in fiber can be compensated automatically. Notably, simulation experimental evaluations are conducted, unveiling the performance of the plug-and-play four-state QKD which is as good as the four-state protocol when having small Rayleigh scattering noise. Furthermore, we conduct a comprehensive analysis of potential security threats targeted at the proposed protocol, thereby substantiating its robustness against such attacks. Moreover, theoretical justifications are provided to validate the security of the proposed scheme.