Wireless Network Model Research Articles

Optimizing Grover's Algorithm for Routing in Quantum Wireless Communication Networks

This paper presents a novel approach to optimizing Grover’s algorithm for addressing the routing problem in quantum wireless communication networks. We begin by conducting a comprehensive analysis of key quantum communication technologies, including quantum teleportation and quantum entanglement swapping, and subsequently construct a quantum wireless communication network model grounded in these principles. Building on this foundation, we design and implement a multi-level quantum wireless network information transmission mechanism, incorporating quantum channels for identity authentication to ensure secure communication. Focusing on the routing challenges in quantum wireless communication networks, we propose innovative applications and optimizations of Grover’s quantum search algorithm. Specifically, we introduce an improved algorithm that efficiently searches for routing paths with maximal metrics within a limited number of hops. This enhancement not only significantly increases routing efficiency but also mitigates the risk of quantum channel disconnection caused by the depletion of entangled quantum pairs, thereby improving the overall success rate of communication. Simulation and experimental results demonstrate that the proposed optimization algorithm substantially enhances the routing performance of quantum wireless communication networks while maintaining robust security measures. This study offers a new solution to the routing challenges in quantum wireless communication networks and significantly optimizes Grover’s algorithm, contributing to the development of more efficient and reliable quantum communication networks.

Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks

Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%.

Lightweight signal recognition based on hybrid model in wireless networks

Lightweight signal recognition based on hybrid model in wireless networks

Guest Editorial: Sustainable Big AI Model for Wireless Networks

Guest Editorial: Sustainable Big AI Model for Wireless Networks

Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing

Implementation of Big AI Models for Wireless Networks with Collaborative Edge Computing

A context-aware energy saving information centric (CAES) model for wireless communication network

The CASE model improves the traditional wireless communication approach by deploying an energy-efficient information-centric algorithm that also enables the tiny low-powered communication nodes of the wireless network to work efficiently with the context-aware energy-saving interface. CAES introduces buffer optimization and a smart routing process among all the low-capacity tiny nodes. Moreover, the gathering of Context-aware energy-saving information also takes care of the performance as well as the management of the traffic load of the entire wireless network. Toward the objectives of the proposed algorithm, which ensure a high ratio of content delivery with an efficient energy saving and energy utilizing scheme, this paper is trying to design a type of Wi-Fi environment that carry the data packet and delivery services carefully without falling down the connection or losing the data contents. In addition to this, this paper concentrate on the measurement of Expected Production Quality (EPQ) values, using loss and quality functions based on various quality parameters to be calculated as one of the measured parts of this research work that defines the ratio of reliability and efficiency of CAES. Further, the implementation of CAES defines the improvement of this research work in the form of managing various quality of communication parameters such as packets delivery ratio, end-to-end Delay and throughput measurement. This manuscript proposes a novel version of the context-aware routing mechanism in a wireless network since traditional wireless communication protocols like SADODV, RAEED-EA are not vulnerable to offer best of communication services. This work aims to manage common communication quality parameters such as Packet Delivery Ratio, End to End Delay and Throughput to be improved using information-centric route management services under denial-of-service (DoS) attacks by implementing context-aware energy saving (CAES), which simultaneously deploys energy efficiency and buffer optimization. This paper simulates and compare the resulting parameters with traditional protocols which defines the importance and role of CAES model for the future of Communication Technology.

Joint Assortment and Cache Planning for Practical User Choice Model in Wireless Content Caching Networks

Joint Assortment and Cache Planning for Practical User Choice Model in Wireless Content Caching Networks

DLLBVS: Design of a High-Efficiency Deep Learning based Low-BER Video Streaming Model for High-Noise Wireless Networks

Design of video streaming models for high-noise networks is a multimodal process that requires efficient channel modelling, noise analysis, error-specific stream control, intelligent data augmentation, etc. This article proposes design of a high-efficiency deep learning based low-BER (Bit Error Rate) Video streaming model for high-noise wireless networks. The proposed model initially uses a Long-Short-Term Memory (LSTM) block for estimation of frame level features, and then uses Orthogonal Frequency Division Multiple Access (OFDMA) based modulation platform for transmission and reception of processed video frames. The OFDMA model is cascaded with a chaotic communication module, which assists in improving data fidelity under different noise conditions. The chaotic communication module is optimized using Grey Wolf Optimizer (GWO), which assists in setting up its hyperparameters for better efficiency under real-time communication scenarios. Video frames were pre-processed using Dual Neural Networks that assisted in estimation of differential frame information sets. Due to estimation of this differential frame information, streaming speed is improved, which assists in increasing number of frames transmitted per second, thereby improving streaming performance for different video types. This frame information is further processed by an iterative Gated Recurrent Unit (GRU) based VARMAx Model, which assists in predicting frame removal instances, thus assisting in faster & low error communications. The GRU VARMAx Model optimizes data flow, thus reducing congestion and improving throughput. The model was tested under AWGN (Additive While Gaussian Noise), Rayleigh, & Rician channel types, and its efficiency was compared with standard streaming techniques in terms of communication delay, Bit Error Rate, Peak to Average Power Ratio (PAPR), throughput, communication jitter and computational complexity levels. Based on this comparison it was observed that the proposed model showcased 3.5% lower communication delay, 8.3% lower BER, 5.9% lower PAPR, 10.5% higher throughput, 3.9% lower jitter and 6.4% lower computational complexity, which makes it highly useful for a wide variety of real-time streaming scenarios.

Power Optimization in Wireless Sensor Network Using VLSI Technique on FPGA Platform

Nowadays, the demand for high-performance wireless sensor networks (WSN) is increasing, and its power requirement has threatened the survival of WSN. The routing methods cannot optimize power consumption. To improve the power consumption, VLSI based power optimization technology is proposed in this article. Different elements in WSN, such as sensor nodes, modulation schemes, and package data transmission, influence energy usage. Following a WSN power study, it was discovered that lowering the energy usage of sensor networks is critical in WSN. In this manuscript, a power optimization model for wireless sensor networks (POM-WSN) is proposed. The proposed system shows how to build and execute a power-saving strategy for WSNs using a customized collaborative unit with parallel processing capabilities on FPGA (Field Programmable Gate Array) and a smart power component. The customizable cooperation unit focuses on applying specialized hardware to customize Operating System speed and transfer it to a soft intel core. This device decreases the OS (Operating System) central processing unit (CPU) overhead associated with installing processor-based IoT (Internet of Things) devices. The smart power unit controls the soft CPU’s clock and physical peripherals, putting them in the right state depending on the hardware requirements of the program (tasks) being executed. Furthermore, by taking the command signal from a collaborative custom unit, it is necessary to adjust the amplitude and current. The efficiency and energy usage of the FPGA-based energy saver approach for sensor nodes are compared to the energy usage of processor-based WSN nodes implementations. Using FPGA programmable architecture, the research seeks to build effective power-saving approaches for WSNs.

A highly efficient resource slicing and scheduling optimization algorithm for power heterogeneous communication networks based on hypergraph and congruence entropy

New services, such as distributed photovoltaic regulation and control, pose new service requirements for communication networks in the new power system. These requirements include low latency, high reliability, and large bandwidth. Consequently, power heterogeneous communication networks face the challenge of maintaining quality of service (QoS) while enhancing network resource utilization. Therefore, this paper puts forward a highly efficient optimization algorithm for resource slicing and scheduling in power heterogeneous communication networks. Our first step involves establishing an architectural description model of heterogeneous wireless networks for electric power based on hypergraph. This model characterizes complex dynamic relationships among service requirements, virtual networks, and physical networks. The system congruence entropy characterizes the degree of matching between the service demand and resource supply. Then an optimization problem is formed to maximize the system congruence entropy through dynamic resource allocation. To solve this problem, a joint resource allocation and routing method based on Lagrangian dual decomposition is proposed. These methods provide the optimal solutions of the nodes and link mappings of service function chains. The simulation results demonstrate that the proposed algorithm in this paper can greatly enhance resource utilization and also meet the QoS requirements of various services.

An improved path loss model for 5G wireless networks in an enclosed hallway

An improved path loss model for 5G wireless networks in an enclosed hallway

An optimized intrusion detection model for wireless sensor networks based on MLP-CatBoost algorithm

An optimized intrusion detection model for wireless sensor networks based on MLP-CatBoost algorithm

Construction of Big Data Analysis Platform for College Students’ Sports Training Driven by Wireless Communication Network

College students’ physical training is an important aspect of their studies and lives. In light of the existing issues with the accuracy of the physical training evaluation index for college students and other issues. Based on the theory of wireless communication networks, model definition and stability analysis methods are used to optimize the original model. The optimization model of a wireless communication network is obtained by considering the data output control theory, trained by college students. This model can analyze the big data of college students’ physical training and construct the corresponding data platform. Through calculations, the rules for data changes under different indicators can be obtained. Finally, the accuracy of the optimized model is verified by comparing it with the original model. Related studies show that the variable parameters in the control system include model parameters, network upper limit, and corresponding control system parameters. On the whole, the model parameters exhibit a consistent trend of change, while the upper limit of the network displays a characteristic three-stage linear pattern. The control system first increases rapidly and then exhibits a gradual decline in exponential function type. The control system and the switching system can be switched using model calculations. The two different change curves exhibit obvious symmetry characteristics when influenced by the output times. It shows that the two change systems have opposite effects on the model, and the overall volatility is relatively high. The wireless communication network model can analyze and build a platform for collecting college students’ sports training data. According to the model calculation, it can be seen that the changes in different indexes exhibit clear linear characteristics. Among them, the concepts of sports, personalized training, and basic training show linearly increasing changes. However, the training structure and content exhibit a typical linear decline. Finally, the advantages of the wireless communication network model are illustrated with the experimental data. This study can provide guidance and ideas for the construction of a data platform for college students’ physical training.

Traffic model in heterogeneous networks based on experimental data

Almost all modern networks provide data transfer between different subscriber devices types using subnets operating in different standards. Therefore, they are heterogeneous. To obtain correct estimates of the load and throughput of heterogeneous networks, it is necessary to be able to simulate such networks traffic close to real. Therefore, the traffic model development in heterogeneous networks based on experimental data is relevant. To do this, the article conducted an experimental study of real traffic in the operator “New Technologies of the XXI Century” heterogeneous network, analyzed the traffic generation main models in wireless networks, mathematical models IPP, 4IPP, IDP and 2IRP were selected for generating traffic in heterogeneous networks and determined the necessary for them application settings. Subscribers are classified depending on the subscriber devices types, the traffic types they use, and the data transferred volume. Based on the experimental data obtained, subscribers are classified depending on the subscriber devices types, the traffic types they use, as well as the data volume they transmit. A list of the subscribers’ main classes has been generated, indicating the models used to generate their traffic, and their parameters have been determined. A traffic mathematical model in heterogeneous networks has been developed, the transmission simulation of real and generated according to the developed model traffic over a heterogeneous network has been carried out, on the basis of which the correctness of the developed model has been shown. The resulting model allows us to obtain correct estimates of heterogeneous networks main characteristics using the mathematical modeling method.

Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions

Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions

A hybrid malicious node detection approach based on fuzzy trust model and Bayesian belief in wireless sensor networks

A hybrid malicious node detection approach based on fuzzy trust model and Bayesian belief in wireless sensor networks

A hybrid malicious node detection approach based on fuzzy trust model and Bayesian belief in wireless sensor networks

A hybrid malicious node detection approach based on fuzzy trust model and Bayesian belief in wireless sensor networks

Wireless Sensor Network (WSN) Model Targeting Energy Efficient Wireless Sensor Networks Node Coverage

Wireless Sensor Network (WSN) Model Targeting Energy Efficient Wireless Sensor Networks Node Coverage

Opportunistic Wiretapping/Jamming: A New Attack Model in Millimeter-Wave Wireless Networks

While the millimeter-wave (mmWave) communication is less susceptible against the conventional wiretapping attack due to its short transmission range and directivity, this paper proposes a new opportunistic wiretapping and jamming (OWJ) attack model in mmWave wireless networks. With OWJ, an attacker can opportunistically conduct wiretapping or jamming to initiate a more hazardous attack based on the instantaneous costs of wiretapping and jamming. We also provide three realizations of the OWJ attack, which are mainly determined by the cost models relevant to distance, path loss and received power, respectively. To understand the impact of the new attack on mmWave network security, we first develop novel approximation techniques to characterize the irregular distributions of wiretappers, jammers and interferers under three OWJ realizations. With the help of the results of node distributions, we then derive analytical expressions for the secrecy transmission capacity to depict the network security performance under OWJ. Finally, we provide extensive numerical results to illustrate the effect of OWJ and to demonstrate that the new attack can more significantly degrade the network security performance than the pure wiretapping or jamming attack.