Wireless Communication Networks Research Articles

A Breakthrough Authentication Protocol for Fortifying IoT-Cloud Security

Abstract: Physical layer key generation, leveraging the reciprocity and randomness of wireless fading channels, has garnered significant research interest in recent years. While theoretical studies have demonstrated its potential to generate informationtheoretically secure keys, challenges remain in translating theory into practice. This paper provides an overview of the physical layer key generation process and discusses its practical challenges. Various passive and active attacks are analyzed and evaluated through numerical studies. A new key generation scheme using random probing signals and combining usergenerated randomness with channel randomness is introduced to counteract active attacks. Numerical results demonstrate that the proposed scheme achieves higher security strength than existing schemes using constant probing signals under active attacks. Secret-key generation relying on wireless channel reciprocity is an intriguing solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS).

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

A Collaborative Allocation Algorithm of Communicating, Caching and Computing Resources in Local Power Wireless Communication Network

IoT Based Antenna Positioning System

An IOT-based antenna’s position system aimed at optimizing connectivity. Through IoT-enabled actuators, the system enables remote adjustment of antenna orientation, managed by a central control unit. Real-time monitoring ensures accurate alignment for optimal signal reception, enhancing communication reliability. Key advantages include remote accessibility, allowing users to control antennas from anywhere with internet connectivity, and scalability, facilitating seamless integration into existing infrastructure. By leveraging IoT technology, the system empowers users with enhanced control over wireless communication networks, offering efficient and reliable antenna positioning solutions across diverse applications.

Intelligent Packet Priority Module for a Network of Unmanned Aerial Vehicles Using Manhattan Long Short-Term Memory

Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances.

ORBITAL ANGULAR MOMENTUM-BASED SLOT ARRAY ANTENNA FOR MODERN APPLICATIONS

Millimeter waves are the most promising waves for different modern wireless communication networks. This is due to their ability to carry big data with enhanced channel capacity. However, mm-waves suffer from low propagation due to the effects of high attenuation factors with high frequencies. Therefore, in this work, a three-dimensional structural antenna design with a slot array is proposed to achieve high gain through a two-port feeding operation for orbital angular momentum applications of modern communication systems. Also, the improved bandwidth of the proposed antenna makes it an excellent candidate for fifth-generation (5G) communications systems. The antenna depends on two concentric structures with conductive circular cross-sectional area. The first structure is shaped as a three-dimensional cone with a cylindrical waveguide. The second structure is shaped as a circular disc with a circular aperture. The two parts of the proposed antenna are concentered to be fed with two discrete ports from the side with a 900phase difference. It is determined that the proposed antenna offers an exceptional gain to vary from 10dBi to 20dBi within the frequency band from 3GHz to 30GHz. It is realized that such enhancement after introducing a conductive circular reflector underneath the conductive cone. The antenna shows a matching impedance below -10dB over the entire frequency band of interest. The antenna radiation proprietors at 25GHz and 26GHz at the E-plane and H-plane are characterized. Finally, the antenna performance is validated using two software packages based on CST studio and HFSS algorithms.

Performance Analysis of Filtered OFDM using MLE for Wireless Communication Networks

Introduction: OFDM has evolved as an effective modulation format for 4G mobile network technologies, Long-Term Evolution, and Worldwide Interoperability for Microwave Access. OFDM is the modulation technique adopted for communication standards such as DSL, wireless LAN, DVB, and DAB. However, because CP-OFDM has considerable out-of-band emission that may interfere with transmissions in adjacent bands and applies a single set of parameters to the whole band to fulfill a given service, it is unable to accommodate the diversity of services. The objective of the research is to develop a novel waveform with better adaptability than CP-OFDM that employs filters to increase spectrum containment. In this view, an FIR filter that uses the Blackman window function to lessen the OOBE is proposed in this manuscript. Filtered OFDM is sensitive to carrier frequency offset (CFO). CFO increases the Inter-Carrier Interference (ICI) in F-OFDM systems. However, research work that combines the CFO compensation technique with filtered OFDM has not been listed in the literature so far. Methods: In this paper, Maximum likelihood estimation is used to cancel the ICI caused by the CFO. The high PAPR of filtered OFDM is one of its drawbacks. The amplitude clipping method is used to reduce the PAPR. The distortion caused by amplitude clipping is prevented by filters used in the FOFDM system. Highlights: • A novel waveform addressing OOBE issues in OFDM is developed for 4G wireless communication networks. • An innovative solution using MLE is proposed to address the challenges faced by the filtered OFDM system due to the CFO. • Article addresses the high PAPR problem of filtered OFDM. method: This work presents a FIR filter that uses the Blackman window function to lessen the OOBE. Filtered OFDM is sensitive to carrier frequency offset (CFO). CFO increases the inter-carrier interference in F-OFDM systems. However, research work is not listed in the literature so far that combines CFO compensation technique with filtered OFDM. In this paper Maximum likelihood estimation is used to cancel the ICI caused by CFO. The high PAPR of filtered OFDM is one of its drawbacks. The amplitude clipping method is used to reduce the PAPR. The distortion caused due to amplitude clipping is prevented by filters used in the F-OFDM system Result: The performance of filtered OFDM with amplitude clipping is analyzed in terms of BER, PSD, and PAPR over the AWGN channel. Conclusion: Simulation results show that, when compared to the traditional OFDM system, such a combined method effectively suppresses PAPR while maintaining good BER (Bit Error Rate) and OOBE performance.

Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles

We introduce an innovative deep learning approach specifically designed for the environment identification of intelligent vehicles under rainy conditions in this paper. In the construction of wireless vehicular communication networks, an innovative approach is proposed that incorporates additional multipath components to simulate the impact of raindrop scattering on the vehicle-to-vehicle (V2V) channel, thereby emulating the channel characteristics of vehicular environments under rainy conditions and an equalization strategy in OFDM-based systems is proposed at the receiver end to counteract channel distortion. Then, a rainy environment identification method for autonomous vehicles is proposed. The core of this method lies in utilizing the Channel State Information (CSI) shared within the vehicular network to accurately identify the diverse rainy environments in which the vehicle operates without relying on traditional sensors. The environmental identification task is considered as a multi-class classification problem and a dedicated Convolutional Neural Network (CNN) model is proposed. This CNN model uses the CSI estimated from CAM exchanged in vehicle-to-vehicle (V2V) communication as training features. Simulation results showed that our method achieved an accuracy rate of 95.7% in recognizing various rainy environments, which significantly surpasses existing classical classification models. Moreover, it only took microseconds to predict with high accuracy, surpassing the performance limitations of traditional sensing systems under adverse weather conditions. This breakthrough ensures that intelligent vehicles can rapidly and accurately adjust driving parameters even in complex weather conditions like rain to autonomous drive safely and reliably.

Spectrum-efficient user grouping and resource allocation based on deep reinforcement learning for mmWave massive MIMO-NOMA systems

Millimeter-wave (mmWave) massive multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) is proven to be a primary technique for sixth-generation (6G) wireless communication networks. However, the great increase in users and antennas brings challenges for interference suppression and resource allocation for mmWave massive MIMO-NOMA systems. This study proposes a spectrum-efficient and fast convergence deep reinforcement learning (DRL)-based resource allocation framework to optimize user grouping and allocation of subchannel and power. First, an enhanced K-means grouping algorithm is proposed to reduce the multi-user interference and accelerate the convergence. Then, a dueling deep Q-network (DQN) structure is proposed to perform subchannel allocation, which further improves the convergence speed. Moreover, a deep deterministic policy gradient (DDPG)-based power resource allocation algorithm is designed to avoid the performance loss caused by power quantization and improve the system’s achievable sum-rate. The simulation results demonstrate that our proposed scheme outperforms other neural network-based algorithms in terms of convergence performance, and can achieve higher system capacity compared with the greedy algorithm, the random algorithm, the RNN algorithm, and the DoubleDQN algorithm.

Joint Energy and Security Optimization in Underwater Wireless Communication Networks

Joint Energy and Security Optimization in Underwater Wireless Communication Networks

Exploration on human resource management and prediction model of data-driven information security in Internet of Things

The advent of the Internet of Things (IoT) has accelerated the pace of economic development across all sectors. However, it has also brought significant challenges to traditional human resource management, revealing an increasing number of problems and making it unable to meet the needs of contemporary enterprise management. The IoT has brought numerous conveniences to human society, but it has also led to security issues in communication networks. To ensure the security of these networks, it is necessary to integrate data-driven technologies to address this issue. In response to the current state of human resource management, this paper proposes the application of IoT technology in enterprise human resource management and combines it with radial basis function neural networks to construct a model for predicting enterprise human resource needs. The model was also experimentally analyzed. The results show that under this algorithm, the average prediction accuracy for the number of employees over five years is 90.2 %, and the average prediction accuracy for sales revenue is 93.9 %. These data indicate that the prediction accuracy of the model under this study's algorithm has significantly improved. This paper also conducted evaluation experiments on a wireless communication network security risk prediction model. The average prediction accuracy of four tests is 91.21 %, indicating that the model has high prediction accuracy. By introducing data-driven technology and IoT applications, this study provides new solutions for human resource management and communication network security, promoting technological innovation in the fields of traditional human resource management and information security management. The research not only improves the accuracy of the prediction models but also provides strong support for decision-making and risk management in related fields, demonstrating the great potential of big data and artificial intelligence technology in the future of enterprise management and security.

Cellular traffic forecasting based on inverted transformer for mobile perception dual-level base station sleep control

Due to the extensive implementation of the fifth generation wireless communication networks (5 G), numerous base stations are being strategically deployed in densely inhabited areas to address the escalating need for network traffic. Within wireless cellular networks, over 80% of energy consumption is primarily concentrated on the side of the base station. In comparison to 4 G base stations, the 5 G base stations exhibit notable enhancements in terms of bandwidth, peak rate, and transmission power. Nonetheless, they suffer from limited signal coverage and extensive resource consumption. To address this challenge effectively, the present study introduces a Mobile Perception Dual-Level (MPDL) base station sleep control strategy based on cellular traffic forecasting. The strategy aims to satisfy the demands for reduced power consumption and enhanced service quality by monitoring both the current network traffic of the base station and the user's mobility, consequently placing the base station into two different sleep states. Specifically, we first designed an inverted Transformer (iTransformer) model to accurately forecast cellular traffic. The model is capable of effectively capturing both the spatial and temporal attributes of cellular traffic. It also takes into account the impact of geographical features, such as Points of Interest (POIs) and the quantity of Base Stations (BSs), on the forecasting outcomes. Consequently, it attains precise predictions of the cellular traffic. Based on the forecasting outcomes of cellular traffic, this study presents a mobility perception dual-level sleep strategy. This strategy proposal differentiates sleep into deep sleep and light sleep, while also taking into account the mobility attributes of users. It incorporates considerations for Quality of Service (QoS) and energy consumption. We assess the performance and application impacts of cellular traffic forecasting and base station sleep strategy in real-world datasets. Several experiments and validations have confirmed that the MPDL, as proposed in this paper, effectively decreases power consumption by a range of 22% to 41% when compared to baselines.

Encapsulation of Energy Efficient, Clustering Algorithm and Spectrum Sensing for Cognitive Radio Based Internet of Things Networks

Since the two decade the Internet of Things (IoT) plays an important role in the field of communication technology. Out of maximum of IoT devices are battery operated. So there should be energy efficient devices that can operate more functions with less power consumption. The main power constraints in the IoT devices are in the communication like spectrum selection, data transmission, etc. To perform communication between the two nodes the spectrum must be available. But as spectrums are limited and a lot of data is to be transmitted there may the issue of spectrum scarcity. The dynamic spectrum access is done using Cognitive Radio (CR) technology that can overcome spectrum scarcity issue. This paper gives the research work on energy efficient, clustering algorithm and spectrum sensing for CR based IoT networks in terms of the methods, merits, demerits and implementation. For efficiency in the spectrum sensing and energy consumption in 5G wireless communication network and data transfer between the IoT devices this study is essential. The biblometric analysis is shown by using VOSviewer to visualize the bibliometric information and the result as an analysis of ROC curve for Rayleigh and Rician channel is plotted using Matlab.

A Novel Approach to Mitigating Packet Loss in Wireless Communication Networks through Machine Learning-Based Adaptive Error Correction

Wireless communication networks are integral to modern computing, facilitating seamless data transmission across diverse devices. Despite their importance, packet loss remains a persistent challenge, adversely affecting network reliability and performance. This research paper introduces a pioneering approach to tackle this longstanding issue, employing machine learning for adaptive error correction. The proposed method aims to significantly enhance the robustness and efficiency of wireless communication systems, presenting an innovative solution that has not been extensively explored in existing literature. Keywords—Wireless communication networks,Packet loss mitigation,Machine learning,Adaptive error correction ,Reinforcement learning,Dynamic decision-making,Real-time network conditions,Signal strength,Interference levels,Robustness,Throughput improvement,Latency reduction,Experimental setup,Performance metrics,Comparative analysis,Statistical significance,Sensitivity analysis,Ethical considerations,Practical implications,Future directions

Influence Factors of Powerline Communication by Artificial Intelligence Approach based on Rock Electric Signal

The field of powerline communication (PLC) has been around for more than a century and uses the power line as a means of information transport. Because it utilizes the current system to transfer data using electrical power, it is advantageous because it prevents the need for new installations. The impact of coal and rock electric signals is examined using the established test methodology, taking into account the coal and rock temperature, characteristics, friction, loading speed, and load size. The main findings can be summed up as follows: Depending on the intensity of the electrical signals produced, the change rule of electrical signals in the coal rock rising with temperature can be separated into three stages. The electrical signals produced by the uniaxial compression of coal-rock mass with varying properties have distinctly different amplitudes. The friction between coal and rock can produce electrical impulses. As loading speed increases, the electrical signal that indicates when the coal rock is prepared to fracture tends to get stronger. The information bottleneck perspective is then used to study information theory and artificial intelligence. In conclusion, we go through a few concepts about the deep integration of AI methods with wireless communication networks. The fundamental aspects of AI that are pertinent to various uses have been briefly addressed at the outset of the paper.

Fast-beam-switching optical phased array for moving objects in wireless optical communication networks.

For optical wireless communication systems, mechanical beam steering struggles to timely switch between multiple users or search for moving users. Here we demonstrate a fast-beam-switching optical phased array (OPA) for agile wireless communication networks. For point-to-multi-point (P2MP) scenarios, a setup of OPA-based fast beam switching between two aligned receivers was developed. A loss-free image transmission experiment was used to demonstrate the stability of switching. Furthermore, we have developed an approach to using the fast-switching OPA to follow the trajectory of moving objects so as to help enable agile random-access switching between moving objects. These results could help offer fast switching and reconfiguration for indoor wireless optical communications.

Regional IP Allocation Techniques Using Drones

Drones, which were initially developed for military applications, have recently been studied and applied to various fields. In this paper, we propose a DANET algorithm that uses a large number of drones to build a wireless communication network infrastructure, and in situations where communication is not possible, such as in disaster areas, we propose a DANET algorithm that uses drones to form a network so that nodes that want to join the network can efficiently acquire IP addresses without collision. In a DANET, a pool of IP addresses is gradually passed to the drones in the next zone in blocks, and the drones in each zone distribute IPs to newly joining nodes, thereby increasing the IP address allocation rate and reducing the IP allocation time to form a temporary but efficient network. Drones assign their own IP addresses through simple Request and Response message exchanges with land-based stations or M-Droin (Mother Droin) in the divided zones that can assign IP addresses. Therefore, DANET can completely eliminate the process of IP collision avoidance (Duplicate Address Detection) and the process of network separation or integration caused by the movement of ships. This paper presents a new possibility for building wireless network infrastructure in unconnected areas such as disaster areas by performing simulations under various conditions to verify the applicability of DANET.

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

The following research focuses on the use of machine learning-based beamforming algorithms to improve Massive Multiple Input Multiple Output (MIMO) systems in 5G networks. Four unique algorithms namely, the Deep Learning Beamforming Algorithm (DLBA), Reinforcement Learning-Based Doa Estimation Algorithm (RLBEA), Clustering based beam forming algorithm(CBA) and GeneticAlgorithm Based Beam Forming Algoeithm were developed after which each of them was undertook evaluation. Widespread trials, in a simulated 5G environment, have revealed that the DLBA and RLBA considerably outperform other technologies by means of system throughput SINR as well Both the DLBA and RLBA achieved high system throughput, increased SINR levels and low BER. CBA and GABA, using clustering and genetic algorithms as their approaches, displayed moderate values on all assessed composite measures. This research offers important insights on the adaptability and learning potential of machine-learning based beamforming algorithms highlighting their ability to improve efficiency in wireless communication networks during the 5G revolution

Improving Routing Performance in Mobile Ad Hoc Networks Using Artificial Neural Networks for Mobility Prediction using deep learning

In this study, a research was done to examine the effectiveness of deep learning models, such as the Artificial Neural Networks, Convolutional Neural Networks, Gated Recurrent Units and Generative Adversarial Networks, to boost routing’s efficiency and performance in the Mobile Ad Hoc Networks through mobility prediction. In total, 10 experiments were conducted, and ANNs demonstrated an average prediction accuracy of 0.86, MAE of 0.10, RMSE of 0.16, and correlation coefficient of 0.93. CNNs showed the most impressive performance, featuring the following indicators: the average prediction accuracy of 0.87, MAE of 0.09, RMSE of 0.15, and the correlation coefficient of 0.94. GRUs, in their turn, displayed quite decent performance, with an average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94; in the meanwhile, GANs can be also seen as offering a decent performance, with the average prediction accuracy of 0.87, MAE of 0.10, RMSE of 0.16, and the correlation coefficient of 0.94. In the end, the identified findings imply that deep learning models can be used to enhance the routing efficiency in MANETs, since they can predict the node movement with the high level of accuracy. The identified changes are possible to lead to the creation of effective wireless communication networks.

Parameter optimization of SQCC-CVQKD based on genetic algorithm in the terahertz band

Recently, we proposed a continuous variable quantum key distribution (CVQKD) scheme based on simultaneous quantum and classical communication (SQCC) in the terahertz (THz) band. It performs classical modulation and quantum Gaussian modulation at the same coherent pulse at the sending end, and an amplifier is used to amplify and demultiplex the signal at the receiving end. However, the previous study set parameters based on prior knowledge which has significant limitations, and as the previous study showed, parameter selection is a crucial task that directly affects the performance of the system. In this paper, we use the genetic algorithm to optimize the parameter selection, and how the different conditions influence the optimal value of parameters is also analyzed. The simulation results show that the parameter optimized with the algorithm can make the scheme achieve a higher secret key rate which greatly improves the applicability of the SQCC scheme in the THz band. This work demonstrates the effectiveness of the scheme to construct wireless quantum communication networks.

Enabling intrinsic intelligence, ubiquitous learning and blockchain empowerment for trust and reliability in 6G network evolution

This study investigates the 6th Generation (6G) wireless communication network as a vital component of Ubiquitous Learning, which makes it possible to build trustworthy relationships and accurate intelligence in a variety of contexts. The important significance of blockchain technology is recognized, and its potential to improve the performance of the 6G network is investigated. Blockchain technology powers Ubiquitous Learning as a decentralized tool. But in Ubiquitous Learning contexts, problems arise when varied communication settings, information asymmetry, and resource limits confront wireless-connected devices functioning as blockchain consensus nodes. To solve these difficulties, which are critical for Ubiquitous Learning, a blockchain sharding and incentive system is presented. The purpose of this technique is to improve the learning process in the 6G network by cultivating reliable intelligence. A sharding architecture that integrates a contract theory-based incentive mechanism and a realistic Byzantine fault tolerance mechanism, particularly tailored for Ubiquitous Learning settings, is described. This model analyses the useful Byzantine consensus mechanism and block broadcast characteristics in wireless networks. This study finds the best contract combination for both completely symmetric and asymmetric information scenarios, optimizing block rewards for customers that seek blockchain services.