Communication Algorithms Research Articles

QPSK circuit optimization Based on Model-Based Design

In order to address the difficult problem of hardware implementation of complex communication algorithm, this paper takes digital phase modulation as an example, and uses model-based design approach to quickly complete the algorithm modeling, simulation and automatic generation of HDL code. Model-based design is a fast and effective design process for algorithm modeling, simulation, testing and automatic code generation. The simulation results show that the model-based design method cannot only quickly accomplish the modeling and testing of the algorithm, but also improve the design accuracy.

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Visible Light Communication

In order to satisfy the large-scale access of visible light communication (VLC) users, as well as the demand for user request rate, the resource allocation problem of visible light communication with drone-assisted non-orthogonal multiple access (NOMA) technique is investigated. An efficient scheme for joint optimization of power allocation and access point (AP) location is proposed. According to the state of user channel information, a user pairing strategy with uniform channel gain difference for any number of users is designed, and an objective function of maximizing the average user data rate with constraints is constructed. For this non-convex NP-hard problem, the optimization problem with constraints is transformed into an optimization problem without constraints by introducing the idea of a penalty function and then solved by the Harris Hawk Optimization (HHO) algorithm based on the nonlinear energy convergence factor, and ultimately, the optimal user power allocation factor, as well as the location of the AP, are found. The simulation results show that the scheme in this paper can improve the average user data rate better compared to other classical schemes. The system performance of the HHO algorithm is improved by about 20.31% compared to the Particle Swarm Optimization (PSO) algorithm. The HHO algorithm based on the nonlinear energy convergence factor improves the convergence speed by about 50% compared to the classical HHO algorithm.

A Simulation and Experimental Platform for Communication Courses Based on Digital Signal Processor

In this paper, a simulation and experimental platform is designed and implemented to overcome the shortcomings of the existing experimental platform for communication related courses. Firstly, the overall scheme of the simulation and experimental platform are introduced, including the specific hardware composition and supporting software. Secondly, the implement details are described in the following order: system composition, peripherals, hardware accelerator, and interface to the host computer. The main system is based on an independently developed digital signal processor, which may relieve the bottleneck problem of imported chips. Using FPGA to implement peripherals enhances the flexibility of the entire system and provides the ability for further development. The feasibility of reducing the cost is then discussed. Thirdly, the design and implementation of the simulator, compiler and assembler, debugger and other tools are introduced respectively. The simulator can operate under normal mode and debugging mode. The former can be used for ordinary experiments for teaching purpose, while the latter is for further development of graduated students. Finally, the prepared experimental projects are shown, including 6 basic experiments and 6 communication algorithm experiments. With the help of these experiments, students can better understand the principles and implementation of corresponding communication algorithms, and experience how different parameters may impact on the communication results, by modifying the configuration of the experimental system. The demonstrations of prototype prove that the simulation and experimental platform can meet the needs of communication related courses. The future work includes developing more experimental projects, raising integration to reduce the costs, and improving the entire system based on student feedback.

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.

Coordination of NPCs in multi-agent systems based on behavior trees

Abstract. This paper addresses the design and coordination challenges of controlling non-player characters' (NPCs) behaviors in multi-agent systems (MAS) using behavior trees (BTs), which are preferred over finite state machines (FSMs) due to their hierarchical structure and ease of maintenance. While BTs effectively resolve the question of "What to do next?" for individual NPCs, their application in MAS, particularly with the integration of a blackboard system for central control, reveals limitations in efficiency, robustness, and heuristic capacity as system complexity increases. To explore solutions to these challenges, this study analyzes various algorithms that enhance the functionality of behavior trees within MAS. The research focuses on three primary areas: the optimization of behavior trees, the development of behavior tree search algorithms, and the improvement of communication algorithms within BTs. Methods involve a comparative analysis of existing and new algorithmic approaches to identify and address inefficiencies in NPC coordination. The findings indicate that advanced behavior tree configurations, when combined with innovative search and communication strategies, significantly improve the coordination, robustness, and operational efficiency of MAS. These enhancements allow for more dynamic and responsive NPC interactions in complex gaming environments.

Optimized DBSCAN with RLA-ODV routing and encryption based on homomartino algorithm for energy efficient and secure V2X communication

Optimized DBSCAN with RLA-ODV routing and encryption based on homomartino algorithm for energy efficient and secure V2X communication

Retraction Note: Energy efficient and intelligent routing algorithm using DAI and self organizing map hybrid algorithm for future optical wireless communication

Retraction Note: Energy efficient and intelligent routing algorithm using DAI and self organizing map hybrid algorithm for future optical wireless communication

Optimizing underwater connectivity through multi-attribute decision-making for underwater IoT deployments using remote sensing technologies

The underwater Internet of Things (UIoT) and remote sensing are significant for biodiversity preservation, environmental protection, national security, disaster assistance, and technological innovation. Assigning tasks to autonomous underwater vehicles (AUVs) is a fundamental challenge in underwater technology and exploration. Remote sensing and AUVs are vital for pollution detection, disaster prevention, marine observation, and ocean monitoring. This work presents an optimized network connectivity using a multi-attribute decision-making approach for underwater IoT deployment. A feature engineering approach highlights the significant characteristics of underwater things, incorporating remote sensing data, and a multi-objective optimization method is used to select optimal UIoT for effective task allocation in deep-sea environments. A balance between data transmission, energy economy, and operational performance is necessary for efficient task distribution. Effective communication algorithms and protocols are needed to maintain environmental sustainability, protect marine ecosystems, and improve underwater monitoring enhanced by remote sensing technologies. Multi-criteria decision-making (MCDM) is beneficial for addressing various challenges in underwater technology, considering factors such as mission objectives, energy efficiency, environmental conditions, vehicle performance, safety, and much more. The proposed criteria importance through intercriteria correlation (CRITIC) methodology will assess technical competencies like communication, resilience, navigation, and safety in an underwater environment, leveraging remote sensing and aiding decision-makers in selecting appropriate undersea devices and vehicles for enhancing communication and transportation. This method prioritizes characteristics and aligns them with specific objectives, improving decision-making quality in the marine environment.

Enhanced intrusion detection with fiber optic sensors in rainy weather conditions: a comparative study of phase parameter matching and cross rebuilding algorithms for higher end communication

Enhanced intrusion detection with fiber optic sensors in rainy weather conditions: a comparative study of phase parameter matching and cross rebuilding algorithms for higher end communication

Priority based inter-twin communication in vehicular digital twin networks

ABSTRACT With the advancement and boom of autonomous vehicles, vehicular digital twins (VDTs) have become an emerging research area. VDT can solve the issues related to autonomous vehicles and provide improved and enhanced services to users. Recent studies have demonstrated the potential of using priorities in acquiring improved response time. However, since VDT is comprised of intra-twin and inter-twin communication, it leads to a reduced response time as traffic congestion grows, which causes issues in the form of accidents. It would be encouraging if priorities could be used in inter-twin communication of VDT for data sharing and processing tasks. Moreover, it would also be effective for managing the communication overhead on the digital twin layer of the cloud. This paper proposes a novel priority-based inter-twin communication in VDT to address this issue. We formulate the problem for priorities of digital twins and applications according to their categories. In addition, we describe the priority-based inter-twin communication in VDT in detail, and algorithms for priority communication for intra-twin and inter-twin are designed, respectively. Finally, experiments on different priority tasks are conducted and compared with two existing algorithms, demonstrating our proposed algorithm's effectiveness and efficiency.

Performance comparison of various end-to-end learning technologies with a bandwidth-limited OWC system

Recently, end-to-end (E2E) learning methodologies have garnered significant attention as a compelling approach to attain global optimal communication within the domain of 6 G native intelligent systems. Nevertheless, a precise evaluation of the diverse E2E techniques is still lacking, leading to uncertainties regarding their applicable scenarios and effectiveness. In this paper, we present a comprehensive comparison applying three advanced E2E methods including the autoencoder-based geometric shaping (AEGS) model, comprehensive autoencoder (CAE) model, and wave-wise auto-equalization (WWAE) model in a real bandwidth-limited optical wireless communication (OWC) system. A novel attention-based comprehensive noise joint channel estimator (ACNJCE) is proposed to serve as a universal channel model adaptable to the existing E2E methods. Based on traditional carrier-less amplitude and phase modulation (CAP) modulation, AEGS, WWAE, and CAE are compared under the conditions of 2 GBaud and 3 GBaud respectively. The final results demonstrate that the CAE exhibits the capability to autonomously allocate bandwidth and achieves the highest dynamic adjustment range, which is increased by 69% compared with CAP based on neural network (NN) equalization. In contrast, AEGS has obvious advantages in terms of received optical power (ROP) gain. Based on bit-power loading discrete multi-tone modulation (DMT) modulation, WWAE can effectively compensate the signal spectrum after modulation order optimization and finally achieves the highest data rate under the condition that the −3 dB bandwidth of the channel is only close to 1 GHz. The BER of WWAE with DMT at this rate is 25.2% of that using the NN equalization. Furthermore, experimental results under turbulent conditions reveal that AEGS exhibits superior and more stable performance amidst the perturbations caused by turbulence due to its ability to achieve end-to-end autonomous optimization while integrating traditional modulation and bringing additional shaping gain. According to our knowledge, this marks the first comprehensive evaluation and comparison of existing major E2E algorithms and traditional communication algorithms in a real OWC system.

Hybrid deep learning with pelican optimization algorithm for M2M communication on UAV image classification

<p>Machine-to-machine (M2M) communication for unmanned aerial vehicle (UAVs) and image classification is essential to current remote sensing and data processing. UAVs and ground stations or other linked devices exchange information seamlessly using M2M communication. M2M connectivity helps UAVs with cameras and sensors communicate aerial pictures in real time or post-mission for image categorization and analysis. During flight, UAVs acquire massive volumes of picture data. Image classification, commonly using deep learning (DL) methods like convolutional neural network (CNN), automatically categorizes and annotates photos based on predetermined classes or attributes. This work uses UAV photos to produce hybrid deep learning with pelican optimization algorithm for M2M communication (HDLPOA-M2MC). HDLPOA-M2MC automates UAV picture class identification. GhostNet model is used to derive features in HDLPOA-M2MC. The HDLPOA-M2MC approach leverages pelican optimization algorithm (POA) for hyperparameter adjustment in this investigation. Finally, autoencoder-deep belief network (AE-DBN) model can classify. The HDLPOA-M2MC method’s enhanced outcomes were shown by several studies. The complete results showed that HDLPOA M2MC performed better across measures.</p>

Performance Analysis of Deep Learning based Signal Constellation Identification Algorithms for Underwater Acoustic Communications

This research delves into the evaluation of Deep learning signal constellation identification (DL-SCI) algorithms in underwater acoustic communications using Orthogonal Frequency Division Multiplexing (OFDM). It distinctly examines at how effective the recurrent neural networks (RNNs), particularly, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) algorithms in predicting the signal constellation when applied to different underwater acoustic channels characteristics. Unlike manual feature selection in machine learning (ML), in this paper, DL-SCI exploits the labelled OFDM signals at the transmitter to detect and decode them at the receiver. In order to measure their effectiveness performance metrics, Bit Error Rate (BER) and parameters derived from the confusion matrix such as accuracy and precision are used. The study highlights the importance of utilizing zero cyclic prefix techniques which can exploit the inherent bandwidth limitation effectively. Furthermore, when examining complexity, it is observed that both GRU and LSTM algorithms require less floating-point operations (FLOPS) compared to traditional methods such as Minimum Mean Square Error (MMSE) and Least Squares (LS). Interestingly GRU shows performance in terms of complexity when compared to LSTM. Moreover, GRU outperforms LSTM by achieving a 4 dB improvement for long subcarriers. These results emphasize the effectiveness of learning techniques in enhancing performance and efficiency in acoustic communications.

The feasibility analysis of load based resource optimization algorithm for cooperative communication in 5G wireless ad-hoc networks

Efficient allocation of resources is crucial in wireless ad hoc networks (WANETs) as spectrum assets are costly. Cooperative communications were introduced as a solution to the problem of limited spectrum availability. In this approach, numerous nodes share their resources and increase the bandwidth available to end-users. This research investigates the practicality of a new algorithm that optimizes resources based on load for Cooperative Communications in 5 G WANETs. The algorithm consists of two components. Initially, a distributed algorithm for forming a topology is suggested. This algorithm employs a load-based approach to explore network conditions and efficiently choose the most suitable topology. An optimization algorithm that relies on a greedy strategy is suggested. In this approach, the chosen nodes send their bits to the receiver to maximize the attainable system throughput. A thorough simulation study is conducted to evaluate the overall performance of the proposed algorithm in assessing existing methods. The proposed model obtained 94.72 % energy efficiency, 91.69 % network throughput, 94.72 % spectrum utilization, 27.47 % network delay, 24.08 % packet loss rate, 94.38 % signal-to-noise ratio, 93.91 % data transfer rate, 95.87 % error detection rate, and 94.28 % link reliability rate. The results demonstrate that the suggested algorithm significantly enhances the system and the overall network performance compared to existing approaches. The proposed approach is feasible and environmentally friendly for optimizing bandwidth in 5 G wireless ad hoc Networks.

Design and Evaluation of a Hybrid 2D-SWZCC OCMDA System with Multicarrier MM-Wave

This paper presents a hybrid communication system combining optical code division multiple access (OCDMA) and multicarrier millimeter-wave (MM-Wave) architecture operating at 35, 55, 75, and 95 GHz connected to a home network center (HNC) and the end user (EU). Hybrid Link reduces congestion on complex RF networks by combining optical fiber backhaul with radio links. The OptiSystem simulation demonstrates successful data transmission and transmission capacity at 40Gbps over various distances. Furthermore, this new 40G-MM wave OCDMA system that takes advantage of a two-dimensional single-weighted zero cross-correlation (2D-SWZCC) system with a multiplexing scheme of polarization wavelength multiplies the same N channels to M OCDMA code of the MM-Wave system that dramatically increased scalability. The comparative analysis shows that the proposed 2D-SWZCC hybrid algorithm outperforms 1D-SWZCC regarding scalability. This study contributes to developing communication algorithms with high data rates and provides promising solutions for method limitations and scalability in hybrid-MM-Wave-OCDMA architectures.

A New Algorithm for Secure Data Communication by Extension of Key-Space Dimension

A New Algorithm for Secure Data Communication by Extension of Key-Space Dimension

Crystals kyber cryptographic algorithm for efficient IoT D2d communication

Crystals kyber cryptographic algorithm for efficient IoT D2d communication

Development of programmable logic controller algorithms for database communication

The present paper provides a brief overview of the possibilities which can arise when outsourc-ing some of the more advanced functionalities usually developed for computers to a programma-ble logic controller. This approach is still being examined to fulfil its aim of improved communi-cation capabilities and provision of effective tools for advanced intelligent control systems. The paper, further, sets out to give an example of one such algorithm which is built upon the MySQL protocol.

Rhizomic Interdisciplinary Model in the Research into the Phenomenon of “New Sincerity”

The article localizes the problem of interscientific system methodology in the study of a new social practice domain, which manifests itself in the form of the trend towards sincerity and frankness in language interaction. The purpose of the article is to consider the issues that arise within the overlapping scientific approaches in the context of polyparadigmality in linguistics. The article describes vectors and scenarios of discipline interaction, as well as the examples of interdisciplinary transfer practice. The author presents the concept of rhizomic interdisciplinarity as a nonlinear development of scientific research that contains the potential for differentiation and integration. The system methodology application has enabled the author to build the core part of the model for describing linguistic facts. It includes some clusters of scientific knowledge that are undergoing a development phase. The article outlines the problems of “new sincerity” with an emphasis on network communication. For illustration, data from social events with elements of a flash mob, cases of cancel culture and eco-alarmism were used. The patterns that form a social community in the form of thematic fashion, Internet communication algorithms, and mobilizing potential are indicated. The author presents several clusters of research into the phenomenon of “new sincerity” from the perspective of the science of language. Being indicative of the dynamics of interaction amongst currently existing approaches and theories, they can form the core of the research model.

EC-MAC: Energy-Aware Cooperative MAC Protocol in Wireless Sensor Network

Nowadays, cooperative communication algorithms have been utilized in Wireless Sensor Network (WSN) to enhance the overall network performance. As is well known, a WSN needs to consider a number of important factors, such as the energy effectiveness and the longevity of the sensor nodes. The energy-aware cooperative medium access control (EC-MAC) protocol is a novel protocol proposed in this paper for usage in WSNs. EC-MAC protocol allows the source nodes to use the intermediate nodes as relays that can be used to transmit the source's data to the access point (AP). This paper demonstrates how the suggested relay selection method can be used by the EC-MAC protocol to choose the best relay node. After channel state information (CSI) has been calculated and acquired, the best relay should have the highest residual energy and the quickest transmission time. Then, by establishing suitable cooperative links, data transmission from a source node to an AP can be carried out. The effectiveness of the EC-MAC protocol in terms of system energy efficiency is examined in this study using the MATLAB simulation tool and compares the outcomes with other cooperative protocols like Modified Cooperative Access MAC Protocol (MCA-MAC) and Throughput and Energy aware Cooperative MAC Protocol (TEC-MAC) and the performance of WSNs employing the suggested EC-MAC protocol is examined in this research for both ideal and dynamic channel conditions. EC-MAC protocol achieved energy efficiency improvements of 20%, and 40% respectively, more than MCA-MAC and TEC-MAC protocols. The results indicated that EC-MAC protocol offers a higher level of energy efficiency for the WSN than other cooperative protocols currently in use.