Wireless Communication Environment Research Articles

An optimization scheme of data link security transmission based on mobile edge computing

For the past few years, the security of data transmission links has been increasingly valued. However, with the increasing requirements for the security performance of data transmission links, how to cut the cost of upgrading while improving security has become a serious challenge. In response to the issue of ensuring low-cost and secure data transmission in wireless communication environments with eavesdroppers, this article innovatively starts with idle roadside units and studies how to incentivize roadside units to transmit interference power to interfere with eavesdroppers, thereby efficiently and economically improving the security performance of mobile user data transmission. Firstly, the impact of roadside unit transmission interference power on the security performance of data transmission links was analyzed by establishing a theoretical model; Then, this paper proposes a three-layer model consisting of ground user groups, eavesdropping users, roadside units, and UAVs equipped with mobile edge computing servers. In this model, by encouraging idle roadside units to send interference power, ground users are assisted to safely unload their own data to mobile edge computing servers. Meanwhile, considering the selfishness of idle roadside units, an improved algorithm based on simulated annealing algorithm is proposed to achieve the optimal mapping between ground users and idle roadside units. Finally, through the analysis of simulation results, this scheme can achieve the goal of reducing system costs while improving the security of data transmission.

Hardware implementation of a mirror array-based optical intelligent reflecting surface for VLC: prototype and experimental results

Faced with growing demands for high-speed and reliable communication systems, optical intelligent reflecting surfaces (OIRS) have recently attracted a lot of interest in visible light communication (VLC). With potential applications in a variety of scenarios, including indoor wireless communications and the Internet of Things (IoT), OIRS is expected to have a transformative impact on optical wireless communications. However, current research is predominantly theoretical, and the hardware implementation of OIRS is insufficient. Therefore, this paper introduces an OIRS prototype based on a mirror array, which is capable of adjusting the reflected lightwave by manipulating the orientation of individual OIRS units to realize an adjustable optical wireless communication environment. Additionally, a hardware platform with a configurable control system for OIRS-based VLC has been developed in this paper. Finally, experimental results demonstrate significant improvements in the amplitude of the received signal and the signal-to-noise ratio of the developed prototype, thereby verifying the enhancement of communication efficiency and the potential of practical OIRS deployment.

Secure authentication in MIMO systems: exploring physical limits

Multiple-input multiple-output (MIMO) technology is employed to improve the reliability and capacity of wireless communication systems. However, the wireless communication environment creates vulnerabilities to spoofing attacks. Furthermore, the authentication challenges posed by the heterogeneous characteristics of wireless applications increase as diverse technologies facilitate the growing number of Internet of Things (IoT) devices. To address these challenges, adaptive physical-layer authentication (PLA) leveraging the inherent antenna diversity in MIMO systems is examined, and an information-theoretic perspective on PLA in MIMO systems is given. The real and imaginary components of the received reference signals are used as attributes with a single-class classification support vector machine (SCC-SVM). It is shown that the authentication performance improves with the number of antennas, and the proposed scheme provides robust authentication.

Building and evaluating the performance of a digital data transmission system over a noisy Rician fading channel using a 16-QAM modulator, Hamming source encoding, and BCH channel encoding

In today's digital age, digital data transmission methods are becoming popular. In a wireless communication environment, interference can cause data loss and distortion. In this paper, the author builds and evaluates the performance of a data transmission system using a noisy Rician fading channel. This system combines the use of 16-QAM modulators, Hamming source coding, and BCH channel coding to improve the reliability of data transmission. The performance evaluation of the system is conducted under different interference conditions. The results show that the proposed system is capable of detecting and correcting errors in transmitted data and has good anti-interference ability.

Contactless Diseases Diagnoses Using Wireless Communication Sensing: Methods and Challenges Survey

Respiratory illness diagnosis and continuous monitoring are becoming popular as sensitive markers of chronic diseases. This interest has motivated the increased development of respiratory illness diagnosis by exploiting wireless communication as a sensing system. Several methods for diagnosing a respiratory illness are based on multiple sensors and techniques. Depending on whether the device embeds the sensor in contact with the body or not, these techniques are commonly categorized as contact based or contactless. Contactless methods have gained increasing popularity due to their ubiquitous nature, non-intrusiveness, and low cost. However, contactless methods are difficult to implement, with several challenges such as dynamic wireless communication environments. This article comprehensively reviews all contactless respiratory illnesses using wireless communication sensing methods, their associated challenges, and issues. In addition, applications of respiratory illness diagnosis methods using wireless communication are provided to investigate each method's potential development and applicability. Continuous and accurate diagnosis of respiratory illness using wireless communication sensing systems can assist caregivers in enhancing the care quality and bestowing patients with more freedom for both inpatients and outpatients. Furthermore, wireless communication monitoring systems could lead to treatment plans remotely more effectively, decrease the duration of patient stays in medical facilities, and reduce overall treatment costs.

Integrated beamforming and trajectory optimization algorithm for RIS-assisted UAV system

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have garnered considerable research interest in the fields of 5G and 6G wireless communication due to their remarkable flexibility and cost-effectiveness. However, the inherent openness of wireless communication environments renders these technologies vulnerable to eavesdropping. This paper presents a penalty-based successive convex approximation algorithm and a minorize–maximization algorithm to optimize the transmission beamforming vector, RIS beamforming vector, and UAV–RIS trajectory. The objective of this study was to enhance the physical layer security performance of wireless communication systems using UAVs and RISs. Our simulation results demonstrate that the proposed technique achieves a higher security transmission rate compared to existing techniques.

Bit error rate analysis of MRC diversity receiver over shadowed Beaulieu‐Xie fading channels for mmWave communications

SummaryThis paper presents the performance of maximum ratio combining (MRC) diversity receiver over shadowed Beaulieu‐Xie (SBX) fading model. The SBX model is used to characterise wireless communication environments that include multiple numbers of line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) signals. The probability density function (PDF) of the MRC scheme over the SBX channel has been derived. The PDF‐based approach has been used to calculate the outage performance and average bit error rate of several modulation schemes for coherent and non‐coherent signals. It has also been shown how the diversity orders and the fading channel's parameters affect the system's performance. To authenticate the evaluated expressions, Monte‐Carlo simulation results are used for validation. The SBX model is used to characterise wireless communication environments that include multiple numbers of LOS and NLOS signals.

Retracted: Evaluation Method and Implementation of Vocal Music Teaching Performance under a Wireless Communication Environment

Retracted: Evaluation Method and Implementation of Vocal Music Teaching Performance under a Wireless Communication Environment

An Effective Intrusion Detection Scheme over Wireless Communication Environment based on Artificial Intelligence Enabled Modified Learning Strategy

based on Artificial Intelligence Enabled Modified Learning Strategy In this present digital world, each and every document need to be in system format as well as preserved into the server end. So, that the transactions between client and server entities are handled by using wireless communication schemes. There are millions of individuals hooked up to the internet enabled network today, and each of them possesses some level of sensitive information. Nowadays, the most valuable asset is knowledge about the organization or its users. This attracts the attackers or hackers to acquire the personal or private information from the server end or else the path that is used for communication. Numerous types of hackers attempt to penetrate a private system in order to obtain access and extract all critical data that could harm the infrastructure, posing a significant threat to the firm. These kinds of attacks are called intrusions and the attackers are termed as intruders. This paper has a detailed study of the past detection scheme and proposed a novel intrusion detection scheme to support users to prevent the data from intruders. The major objective of this paper is to design a novel Intrusion Detection System with full of attack handling and identification capabilities as well as prevent the data safe in server end without give a lead to intruders to make an attempt. The motto of this work is to introduce a modified learning strategy with respect to the conventional learning scheme called Artificial Neural Network (ANN) and provides a robust support and resistance to identify the intruders over the wireless communication environment. To make the users more convenient to preserve the data over the remote server environment and make transactions in secured manner with the help of the proposed learning scheme. These objectives are clearly illustrated with graphical representations over the resulting section of this paper. This paper introduced a modified learning scheme called Artificial Intelligence Enabled Modified Learning Strategy (AIeMLS), in which it provides a support to genuine network users to carry on with their private data as well as preserve the data over the server end without any threats. The proposed logic called AIeMLS is derived from the conventional learning scheme called ANN, in which it is composed of a number of processing components that accept inputs and generate outputs according to their predetermined activation functions and it is being used to characterize complex situations and to anticipate the contents of specified model parameters based on their learning data. By modifying the last layer of the ANN and adapt the logic of Support Vector Machine (SVM) into it to customize the model into a novel design. This is helpful to identify the intrusions in an intellectual manner with maximum level of accuracy and performance ratio.The proposed modified learning approach AIeMLS attains the maximum accuracy ratio of 98.5% in an outcome, whereas the other conventional algorithms such as ANN and SVM attain 98% and 96% accuracy in results. These resulting performance and efficiency is portrayed in the resulting section using graphical outputs.This approach of identifying the intrusions over the wireless communication environment is helpful to users in real-time environment and significantly preserves the data in proper manner. In future the work can further be enhanced by means of adding some crypto security features to enhance the security level more in the proposed work.

Adversarial Attack and Defense on Deep Learning for Air Transportation Communication Jamming

Air transportation communication jamming recognition model based on deep learning (DL) can quickly and accurately identify and classify communication jamming, to improve the safety and reliability of air traffic. However, due to the vulnerability of deep learning, the jamming recognition model can be easily attacked by the attacker’s carefully designed adversarial examples. Although some defense methods have been proposed, they have strong pertinence to attacks. Thus, new attack methods are needed to improve the defense performance of the model. In this work, we improve the existing attack methods and propose a double level attack method. By constructing the dynamic iterative step size and analyzing the class characteristics of the signals, this method can use the adversarial losses of feature layer and decision layer to generate adversarial examples with stronger attack performance. In order to improve the robustness of the recognition model, we use adversarial examples to train the model, and transfer the knowledge learned from the model to the jamming recognition models in other wireless communication environments by transfer learning. Simulation results show that the proposed attack and defense methods have good performance.

Deep neural network-based clustering algorithm for multiple flying reconfigurable intelligent surfaces-supported bulk systems

Recently, as data demand has increased owing to the rapidly increasing demand for wireless devices and the influence of data traffic, various technologies are being developed to support it. Among them, millimeter-wave (mmWave) frequencies with rich spectra and high data-transmission rates suffer from the problem of large path loss. Accordingly, there is a growing interest in unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which can be utilized advantageously to reconstruct wireless communication environments. Therefore, this work considers a large-scale system comprising a number of users and Flying RISs, combining UAVs and RISs to increase algorithm utilization. We propose a deep neural network-based algorithm that places Flying RISs in an appropriate location so that they can support as many users as possible. Simulation results confirmed that the proposed technique could place Flying RISs in an efficient location with higher accuracy and speed in large-scale systems compared to existing techniques.

Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems

AbstractSo far, various data‐driven approaches have been presented to obtain channel state information (CSI) in millimeter wave multiple‐input‐multiple‐output wireless networks. In almost all previous works, training and testing channels were assumed to have the same distribution, which may not be the case in practice. In this article, we address this challenge by proposing a learning framework that is a combination of a recurrent neural network (RNN) model and a deep neural network (DNN) for estimating CSI in a dynamic wireless communication environment. Furthermore, we use federated learning to train the learning‐based channel estimation model. More specifically, we introduce a two‐stage downlink pilot transmission procedure, where in the initial stage, long frame length downlink pilot signals are used to train the introduced RNN‐DNN model. Following that, users will receive shorter‐frame‐length pilot signals that can be used for CSI estimation. To speed up the training procedure of the proposed network, we first generate a pre‐trained model and then modify it according to the collected data samples. Simulation results demonstrate that, when the channel distribution is unavailable, the proposed approach performs significantly better than the most recent channel estimation algorithms in terms of estimation performance and computational complexity.

A Survey Paper on IoTAgroFlow Advancing Agriculture with Smart Monitoring and Irrigation Systems

Precipitation and climate change have fluctuated over the past ten years. As a result, a large number of Indian farmers have recently embraced climate-smart farming practices known as "smart agriculture." Among the most significant uses of IOT is smart agriculture. This boosts yield while wasting less water and fertilizer. Smart agriculture refers to the application of Internet of Things (IoT)-based automated and controlled information technology. IOT is widely used in all wireless communication environments and is growing quickly. Based on the actual circumstances of the agricultural system, this project studied and investigated the integration of IOT technology, sensor technology, and wireless networks. soil temperature, relative humidity, and pH are measured by temperature, humidity, and pH sensors. a hybrid strategy combining wireless and Internet connectivity.

A Novel Routing Control Method Using Federated Learning in Large-Scale Wireless Mesh Networks

Currently, the volume of communication by mobile terminals are increasing owing to 5G and other technologies. A robust network and appropriate routing control methods are requied to transmit information in unstable wireless communication environments and avoid congestion. Therefore, in recent years, numerous studies have been conducted on wireless mesh networks (WMNs), which provide a fault-tolerant communication environment by securing multiple communication paths and whose topology can be freely configured and extended. Additionally, machine learning routing is attracting attention as a new routing method for wireless communication environments. However, when performing machine learning on a large WMN, the learning time increases and rapid routing control may be impossible. In this study, we apply federated learning to machine learning and propose a machine-learning-based routing method that can be applied to large-scale WMNs. Furthermore, experimental results demonstrate the effectiveness of the proposed method in various environments: congestion avoidance is achieved in a large-scale WMN by machine-learning routing using federated learning. This study is expected to serve as a basis for significant progress in the realization of large-scale WMNs as wireless communication infrastructure.

PDF Comparison based on Various FSO Channel Models under Different Atmospheric Turbulence

Recently, wireless communication environments with high speeds and low complexity have become increasingly essential. Free-space optics (FSO) has emerged as a promising solution for providing direct connections between devices in such high-spectrum wireless setups. However, FSO communications are susceptible to weather-induced signal fluctuations, leading to fading and signal weakness at the receiver. To mitigate the effects of these challenges, several mathematical models have been proposed to describe the transition from weak to strong atmospheric turbulence, including Rayleigh, lognormal, Málaga, Nakagami-m, K-distribution, Weibull, Negative-Exponential, Inverse-Gaussian, G-G, and Fisher-Snedecor F distributions. This paper extensively studies and analyses different probability density functions (PDFs) that govern the FSO channel, considering various channel models. This paper aims to comprehensively understand how FSO channels can be effectively modeled using different PDFs. Accurate modeling is crucial for designing FSO systems that can operate optimally under potential environmental conditions. Selecting the appropriate PDF model plays a crucial role in determining the FSO channel's performance during system design. With a multitude of PDF models available, this study aims to identify the most effective PDF model to be employed in FSO channel modeling.

Retracted: Innovation and Development of University Education Management Informationization in the Environment of Wireless Communication and Big Data

Retracted: Innovation and Development of University Education Management Informationization in the Environment of Wireless Communication and Big Data

Energy efficiency optimization algorithm based on RIS and D2D for multi-user MIMO system

Reconfigurable Intelligent Surface (RIS) is composed of reconfigurable passive components, which can realize on-demand customization of wireless communication environment. Device-to-Device (D2D) communication is one of the essential technologies to improve the capacity and spectrum efficiency of network system. In this paper, we propose an energy efficiency optimization algorithm based on RIS and D2D assisted multi-user MIMO system. The purpose is to maximize the energy efficiency of the system by jointly optimizing subcarrier allocation, power allocation and passive beamforming at RIS. We first formulate the energy efficiency maximization problem with the constraints of different user rate, power, subcarrier allocation and phase. Since it is a non-convex problem, we use an alternating optimization (AO) algorithm to decouple it into three sub-problems. The solutions of these three sub-problems are solved by subcarrier allocation based on channel strength, SCA algorithm, Dinkelbach algorithm and Riemannian gradient algorithm respectively. Simulation results show that the proposed scheme can significantly improve the energy efficiency.

Towards load balancing in IoV system: A vehicle-assisted batch verification scheme

The Internet of Vehicles (IoV) can improve the efficiency of transportation systems while enhancing the passenger travel experience. However, due to the open wireless communication environment of the IoV, the transmission of data faces the threats of eavesdropping, forging and tampering. Fortunately, the concept of Vehicle-assisted Message Verification Schemes (VMS) are proposed which not only ensures the integrity and authenticity of message but also alleviates the burden of original verification nodes such as Roadside Units (RSUs). Nevertheless, since the individual rationality of vehicles, not all vehicles are willing to participate the message verification tasks. Besides, there may be an overlap in sensory ranges of vehicles that results in redundant verification data. Such two challenges greatly hinder the development of the VMS. Therefore, we present LBABV, a vehicle-assisted batch verification scheme towards load balancing in the IoV system. Firstly, we propose a novel batch verification scheme to enhance the efficiency of messages verification. Then, based on the proposed batch verification scheme, an assisted nodes selection algorithm with incentive mechanism is designed to select a suitable set of vehicles for parallel verification. In this way, the problems of redundant verification as well as individual rationality are elegantly addressed. Theoretical analysis demonstrates the security of our proposal. In the meanwhile, the simulation results show that the system total delay of our LBABV is reduced by 75% compared to batch verification and 81% compared to individual verification, respectively.

Secure transmission for reconfigurable intelligent surface assisted communication with CV‐DDPG

AbstractReconfigurable intelligent surface (RIS) has great potential in securing wireless transmission, and it can flexibly change the wireless communication environment. However, the employment of RIS increases the difficulty in beamforming of base station (BS), and existing schemes cannot be directly utilized to enhance the security of system. Therefore, a reinforcement learning framework to jointly control BS and RIS for the purpose of enhancing the secure performance is proposed. Specifically, the beamforming ia transformed with artificial noise and reflection control as a Markov decision problem (MDP) in complex domain. Then, we develop the deep deterministic policy gradient based on complex‐valued neural networks (CV‐DDPG) to imitate the rules of complex computation. Additionally, the CV‐DDPG is performed as the agent of reinforcement learning to control the BS and RIS, which enables better utilization of the implied phase information of complex values in the system. The simulation results show that the proposed model promotes the performance of the control and greatly improves the secrecy rate of the legitimate user.

IRS-Driven Cybersecurity of Healthcare Cyber Physical Systems

Intelligent reflective surface (IRS) as one of the most potential 6G key technologies injects new vitality into the smart medical and health system by intelligently configuring the wireless communication environment and spawns the construction of a modern medical information system. Medical data security is a sensitive issue that hospitals are generally concerned about in recent years. There are great hidden dangers to information security in the environment of “cloud storage”. The IRS provides a new solution for enhancing the privacy and security of “cloud storage” medical file data, assisting in the efficient transmission and interconnection of information, and ensuring highly secure and uninterrupted services in healthcare-based cyber physical systems (CPS). Specifically, the modelling in this paper considers the situation where communication over multiple-input single-output (MISO) channel is eavesdropped by a patient spy holding a single antenna receiver, and additionally the patient spy is obscured by an obstacle. One of the multi-antenna base stations (BS) transmits independent data streams to doctors and keeps them secret from patient spies. By jointly optimizing the beamforming at the BS and the phase shift of the IRS reflection elements, the simulation analysis verifies that the introduction of IRS can effectively improve the privacy security of medical data.