Communication Algorithms Research Articles

Single longitudinal mode fiber laser with optimized compound ring cavity using genetic algorithm for optical fiber communication

Single longitudinal mode fiber laser with optimized compound ring cavity using genetic algorithm for optical fiber communication

A hybrid deep learning and quantum computing approach for optimized encryption algorithms in secure communications

As online dangers get worse, there is a greater need for strong encryption methods to protect private conversations. Utilizing the strengths of both deep learning and quantum computing, this study suggests a new mixed method for improving the security of communication systems by making encryption algorithms work better. When it comes to keeping up with new online threats, traditional security methods often fall behind. Deep learning techniques could be a good way to improve encryption algorithms because they let the system learn and change to new attack methods. In the meantime, quantum computing offers unmatched computing power that can completely change how cryptography works by using quantum events like superposition and entanglement. Our suggested method combines the flexibility of deep learning with the computing power of quantum computing to get around the problems with current encryption methods. This will make safe communication systems more resistant to attacks from smart people. Through tests and models, we show that our mixed approach works better and more effectively than current encryption methods. This shows that it has the ability to solve the growing safety problems in a world that is becoming more and more linked.

Blind Equalization Optimization Algorithm for FTN Optical Wireless Communication over Exponential Weibull Channel

Blind Equalization Optimization Algorithm for FTN Optical Wireless Communication over Exponential Weibull Channel

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

Adaptive Resource Allocation Algorithm for 5G Vehicular Cloud Communication

A Proactive Resource Allocation Algorithm for UAV-assisted V2X Communication Based on Dynamic Multi-objective Optimization

A Proactive Resource Allocation Algorithm for UAV-assisted V2X Communication Based on Dynamic Multi-objective Optimization

Research on Optimization Strategies of Algorithmic Models for Digital Visual Communication in Cultural Heritage Protection

Abstract With the development of digital technology, digital visual communication has more diversified forms in cultural heritage protection. This paper proposes to design a series of digital commemorative collection tickets for the Dong painting based on the theme. In the visual aesthetic evaluation, the optimization strategy of the digital visual communication algorithms model for cultural heritage protection is evaluated. In the optimization of image enhancement, based on the Retinex algorithm, we propose a single-scale algorithm that achieves the edge enhancement and dynamic range compression of the image and a multi-scale algorithm that meets the requirements of enhancing the details of the image and preserving the color at the same time and introduces the color recovery factor C to avoid the phenomenon of color distortion of the picture. Based on the K-means algorithm and Gaussian mixture model, the optimization of the way of selecting the initial clustering center and determining the number of class clusters is done to improve the efficiency of color feature extraction. Based on the optimized algorithmic model, the evaluation score of the Dong painting product of intangible cultural heritage designed in this paper is 6.95, which is better than the other comparative products. And the total evaluation value of all design elements in the product is more than 80.

The Construction of Safety Education System in Colleges and Universities Based on Network Communication Algorithm

Abstract In the information network, using the characteristics of information diffusion of the Internet, information pushing can be carried out effectively to help publicize safety education, which is of great significance to the construction of safety education system in colleges and universities. In this paper, we optimize the distribution of nodes through graph transformation and independent set ideas and propose an influence maximization algorithm based on HISS to improve the usability of safety information. On this basis, an adaptive safety education system is designed, and an optimization algorithm is proposed for determining the probability of content and knowledge point mastery, which is applied to actual safety education. It was found that the total score of the safety test of the experimental class was improved by 16.22 points before and after, the total score of the experimental class exceeded that of the control class by 10.49 points, and the p-values were all less than 0.001. In total, 84.53% of the students strongly agreed or agreed that the types of learning resources could satisfy their learning needs, and 82.29% of the students were satisfied or very satisfied with the way of pushing the resources of the online course. It indicates that improving the safety education system can increase students’ motivation to learn safety, promote the mastery of safety skills, and cultivate safety awareness.

Удосконалення процесу прийняття управлінських рішень в Україні шляхом забезпечення їх публічності в умовах війни

Problem statement. The requirement to make timely and correct administrative decisions is relevant for public management at any level, in all countries and in all historical periods. Therefore, many managers wonder how to develop a mechanism for making such a decision. There are conditions in which the administrative decision-making process acquires additional complications. In the conditions of martial law, the improvement of such a process acquires certain specific features. It is affected mainly by the safety and value components of social life, as well as the change in the algorithms of interaction between society and authorities in the specific conditions of war. An additional factor that provokes the need to revise the administrative decision-making process in Ukraine is the implementation of systemic reforms in the context of fulfilling the requirements for our country’s accession to the EU and NATO. Therefore, improving the administrative decision-making process in Ukraine, ensuring their publicity, is becoming particularly acute. Presentation of basic material. The article analyses the administrative decision- making process under martial law in Ukraine through the principle of publicity, specific requirements for the publicity of information management and administration via the prism of security requirements, and interaction with the Armed Forces. It substantiates that the publicity principle ensuring the formation and implementation of administrative decisions at management levels is a European integration requirement. Based on the analysis of normative and legal instruments it establishes that organisational, institutional, and legal requirements for the authorities’ activity provide for and oblige them to follow the publicity principle while adopting administrative decisions under martial law. Possible restrictions on public information disclosure are analysed and defined. It is revealed that restrictions relate to the issues of defence means and forces of Ukraine, but not to the processes of making administrative decisions, the public information holder is responsible for the information content in the decision-making process, and it is stipulated by the legislation. It is shown that the administrative decision-making process improvement in Ukraine by ensuring its publicity under martial law is achievable through several strategies (digitalisation, financing control, electronic reporting development, revision and improvement of crisis communication algorithms between the population and authorities, civil society institutions development, in particular, motivation of people to participate in making managerial decisions, enhancing officials’ skills in communicating with the population based on the publicity and compliance balance with the requirements of the martial law on access to information, etc.). Conclusions and suggestions. A conclusion is made that Ukraine is developing during wartime in line with European managerial approaches, among which is publicity in making managerial decisions by the authorities. Among the promising directions of research, we see the issue of cooperation with international organizations and partners to strengthen efforts to ensure publicity during the administrative decision-making process at various levels; exchange of publicity practices with democratic countries that have experience in decision-making during military conflicts.

Analysis of the radio signal distribution for controlling unmanned aerial vehicles

It is of great importance to create a radio communication algorithm for the communication of flight units with ground stations and the exchange of information. To this end, this article analyzes the interference in the path of propagation and propagation of the radio signal for flight control.

An Algorithm Design for a Secure Full Duplex Non-Orthogonal Multiple Access in Wireless Communication System

In this article, a new Component-Forward (CF) cooperative communication algorithm is proposed for Full Duplex Modulation-based Non-Orthogonal Multiple Access and is referred as FD-MNOMA. The FD-MNOMA prevents security issues in the FD mode of a cooperative communication system, where a near user assists a far user to receive a better quality of signal. For further improving the security issues, Artificial Noise (AN) is also added to the signal and called it FA-MNOMA. In this article, the secrecy capacity equation is derived for the FA-MNOMA algorithm. Performance evaluation shows that FA-MNOMA and FD-MNOMA outperforms the traditional NOMA. The secrecy capacity of FA-MNOMA is further optimized using Machine Learning Genetic Algorithm (MLGA). This article also shows the implementation of FA-MNOMA in the Intelligent Transportation System (ITS) as one of its applications.

A high dynamic carrier acquisition and tracking algorithm for space coherent optical communication

In coherent optical communication, the Doppler frequency offset reaches several GHz, which seriously affects the performance of the coherent demodulation system and increases the bit error rate. In this paper, a novel carrier acquisition and tracking algorithm for spatial coherent optical communication is proposed. The algorithm adopts the dual-locked frequency loop that the CPAFC tracking algorithm is effectively combined with the ODAFC tracking algorithm. The CPAFC tracking algorithm can be used for frequency offset gradually change capture and tracking, and the ODAFC tracking algorithm can capture large frequency offset, and according to the locked state control the working state of the two FLL loops. In the condition of stable locked, use only the CPAFC tracking algorithm to capture the gradually changing frequency offset; in the other condition, use two FLL loops together. The experimental results show that the 5Gbps BPSK laser communication performance loss is less than 1.5 dB when the Doppler frequency offset is 2.5 GHz, and the carrier frequency offset change rate is 20 MHz/s.

Multitask and Multiobjective Joint Resource Optimization for UAV-Assisted Air-Ground Integrated Networks Under Emergency Scenarios

To face the challenges in emergency scenarios, a multi-task and multi-objective optimization algorithm for computation offloading and relay communication is investigated for the air-ground integrated networks, composed of unmanned aerial vehicles (UAVs), emergency vehicle users (EVUs) and ground sensor nodes (GSNs). We propose an HFL-DDQN algorithm, which combines horizontal federated learning (HFL) with double deep Q-network (DDQN). First, UAVs are separated into two clusters according to the services they provide, i.e., edge computing or relay communication. Next, the optimization problems are formulated for two types of services respectively. For the computation offloading tasks of EVUs, the optimization objective is to minimize the weighted sum of delay and energy consumption. For the sensor data transmission of GSNs, the optimization objective is to maximize the minimum rate of relay links. We define the total cost of the system as the sum of two types of services. Then, federated aggregation is used to joint training the global neural networks model without sharing raw data. Furthermore, the DDQN is improved by adopting prioritized experience replay to achieve better convergence. The simulation results show that the proposed HFL-DDQN algorithm not only outperforms the state-of-art baselines in terms of the system cost, but also promotes the generalization in execution process, which is especially applicable to the rescue scene under accidents.

Analysis of High-Frequency Communication Channel Characteristics in a Typical Deep-Sea Incomplete Sound Channel

Deep-sea acoustic communication has attracted widespread attention in recent years. Research on deep-sea acoustic communication channel characteristics is essential for the development and system design of deep-sea acoustic communication technologies. However, the structural and spatiotemporal characteristics of deep-sea high-frequency acoustic communication channels (unlike those of shallow-water acoustic communication channels) are poorly understood. Based on a channel measurement experiment in a typical deep-sea incomplete sound channel environment in the South China Sea, this paper analyzes the structural and spatiotemporal characteristics of high-frequency underwater acoustic channels in the direct-arrival and shadow zones. The channel multipath vertical structure, amplitudes of different path clusters, root mean square (RMS) delay spread, and channel temporal coherence are investigated at different depths. The randomness of different path clusters is quantified as the coefficient of variation. The channels in the shadow zone are characterized by a complex multi-path structure, high RMS delay spread, and low temporal coherence. On the contrary, the channels of the direct-arrival zone show multi-path convergence, low RMS delay spread, and high temporal coherence. Understanding the dynamic changes in these parameters can guide the statistical modeling of channels and the design of communication algorithms in different zones in deep-sea high-frequency acoustic communication scenarios.

Algorithm development for Vehicle-To-Vehicle (V2V) communication

This paper presents the development of an algorithm for Vehicle-to-Vehicle (V2V) communication, a crucial technology in Intelligent Transportation Systems (ITS) that holds significant potential for enhancing road safety and traffic efficiency. One of the most common types of vehicle collisions occurs at intersections, particularly those without traffic lights. This study focuses on creating a V2V algorithm designed to prevent collisions in such scenarios. The findings were presented through visual simulations that depict various scenarios involving vehicles approaching an intersection. The algorithm follows a two-step process: Firstly, it utilizes Dedicated Short-Range Communication Systems (DSRCS) to accurately estimate the distance between vehicles. Leveraging this distance information, the algorithm dynamically adjusts the speed of each vehicle. The algorithm’s performance is assessed using Convolutional Neural Networks (CNN), which enables a comprehensive evaluation of its reliability and efficiency in V2V communication. The algorithm demonstrates notable enhancements in the reliability and efficiency of V2V communication. This paper serves as a validation of the feasibility of developing more advanced V2V communication algorithm and potentially making significant contributions to the advancement of ITS.

Optimization of Personal Credit Evaluation Based on a Federated Deep Learning Model

Financial institutions utilize data for the intelligent assessment of personal credit. However, the privacy of financial data is gradually increasing, and the training data of a single financial institution may exhibit problems regarding low data volume and poor data quality. Herein, by fusing federated learning with deep learning (FL-DL), we innovatively propose a dynamic communication algorithm and an adaptive aggregation algorithm as means of effectively solving the following problems, which are associated with personal credit evaluation: data privacy protection, distributed computing, and distributed storage. The dynamic communication algorithm utilizes a combination of fixed communication intervals and constrained variable intervals, which enables the federated system to utilize multiple communication intervals in a single learning task; thus, the performance of personal credit assessment models is enhanced. The adaptive aggregation algorithm proposes a novel aggregation weight formula. This algorithm enables the aggregation weights to be automatically updated, and it enhances the accuracy of individual credit assessment by exploiting the interplay between global and local models, which entails placing an additional but small computational burden on the powerful server side rather than on the resource-constrained client side. Finally, with regard to both algorithms and the FL-DL model, experiments and analyses are conducted using Lending Club financial company data; the results of the analysis indicate that both algorithms outperform the algorithms that are being compared and that the FL-DL model outperforms the advanced learning model.

Coasting advice based on the analytical solutions of the train motion model

Supervision, data analysis and communication algorithms monitor trains, exploiting most of their available computational power. On-board eco-driving algorithms such as Driver Advisory Systems (DAS) are no exception, as the computational power available limits their complexity and features. This was the case of Roltijd, the in-house developed DAS based on coasting advice of NS, the main Dutch passenger railway undertaking. This platform calculated the coasting curves at every second by integrating the equations of motion numerically, assuming that the track is flat. However, generating more complex driving advice required replacing this coasting curve calculation by a more computationally-efficient algorithm. In this article we propose a new coasting advice algorithm based on the analytical solutions of the train motion model, assuming that gradients and speed limits are piecewise constant functions of the train location. We analyse the qualitative properties of these solutions using bifurcation theory, showing that bifurcations arise depending on the value of the gradient and the applied tractive effort. We validate the proposed algorithm, finding that our algorithm is accurate and can be 15 times faster than the previous method. This allowed NS to implement our algorithm on their trains, contributing daily to the sustainable mobility of 1.3 million passengers.

Fuzzy Enhanced Black Widow Spider with Secure Encryption Random Permutation Pseudo Algorithm for Energy Efficient Cluster Communication in WSN

Devices connected to the Internet of Things (IoT) are increasingly being used. Modern mobile apps may be incorporated onto low-cost, low-power devices with the help of these extensions. This integration is made possible by the utilization of low-cost, low-power sensor nodes. All sensor nodes transmit data either directly or via a multi-hop path to other receivers or nodes. In order to enable effective inter-cluster communication through ideal cluster head selection, we build a fuzzy-enhanced black widow spider based on the Secure Encrypted Random Permutation Pseudo Algorithm (FEBWS-SERPPA) in this study. By taking energy, latency, and distance characteristics into account, the suggested FEBWS-SERPPA algorithm enhances the black widow spider optimization method by choosing the optimal cluster head in the cluster. To ensure its effectiveness, the suggested FEBWS-SERPPA algorithm's performance is compared to the state-of-the-art. The suggested FEBWS-SERPPA algorithm delivers enhanced performance speed with very low energy consumption and extended network lifetime when compared to existing cutting-edge methods.

Energy-efficient device-to-device communication in internet of things using hybrid optimization technique

<span lang="EN-US">Device-to-device (D2D) communication has grown into notoriety as a critical component of the internet of things (IoT). One of the primary limitations of IoT devices is restricted battery source. D2D communication is the direct contact between the participating devices that improves the data rate and delivers the data quickly by consuming less battery. An energy-efficient communication method is required to enhance the communication lifetime of the network by reducing the node energy dissipation. The clustering-based D2D communication method is maximally acceptable to boom the durability of a network. The oscillating spider monkey optimization (OSMO) and oscillating particle swarm optimization (OPSO) algorithms are used in this study to improve the selection of cluster heads (CHs) and routing paths for D2D communication. The CHs and D2D communication paths are selected depending on the parameters such as energy consumption, distance, end-to-end delay, link quality and hop count. A simulation environment is designed to evaluate and test the performance of the OSMO-OPSO algorithm with existing D2D communication algorithms (such as the GAPSO-H algorithm, adaptive resource-aware split-learning (ARES), bio-inspired cluster-based routing scheme (Bi-CRS), and European society for medical oncology (ESMO) algorithm). The results proved that the proposed technique outperformed with respect to traditional routing strategies regarding latency, packet delivery, energy efficiency, and network lifetime.</span>

Multi-Band Antenna Design Optimization Using Nature-Inspired Evolutionary Algorithm for 5G Wireless Communication

Automation and telecommunication systems are increasingly improving and becoming more easily integrated thanks to wireless communication technology, in which the essential component is the antenna. Designing and optimizing antenna structures still takes a lot of time, especially for complex structures with multiple operating frequency bands or complex material layers. With modern computing power, the speed of computation is increasingly efficient, and combining automated computation for optimization and design is entirely feasible. Many research groups around the world have implemented automated antenna design through optimization algorithms, genetic algorithms, swarm algorithms, collectively referred to as evolutionary algorithms. These algorithms simplify the antenna design process, making it more automated and optimized. In this paper, the Improved Black Hole algorithm is proposed and used to optimize the design of a CPW-Fed slot microstrip antenna.

SUARA: A scalable universal allreduce communication algorithm for acceleration of parallel deep learning applications

Parallel and distributed deep learning (PDNN) has become an effective strategy to reduce the long training times of large-scale deep neural networks. Mainstream PDNN software packages based on the message-passing interface (MPI) and employing synchronous stochastic gradient descent rely crucially on the performance of MPI allreduce collective communication routine.In this work, we propose a novel scalable universal allreduce meta-algorithm called SUARA. In general, SUARA consists of L serial steps, where L≥2, executed by all MPI processes involved in the allreduce operation. At each step, SUARA partitions this set of processes into subsets, which execute optimally selected library allreduce algorithms to solve sub-allreduce problems on these subsets in parallel, to accomplish the whole allreduce operation after completing all the L steps. We then design, theoretically study and implement a two-step SUARA (L=2) called SUARA2 on top of the Open MPI library. We prove that the theoretical asymptotic speedup of SUARA2 executed by P processes over the base Open MPI routine is O(P). Our experiments on Shaheen-II supercomputer employing 1024 nodes demonstrate over 2x speedup of SUARA2 over native Open MPI allreduce routine, which translates into the performance improvement of training of ResNet-50 DNN on ImageNet by 9%.