Wireless Communication Environment Research Articles

Progressive Unsupervised Domain Adaptation for Radio Frequency Signal Attribute Recognition across Communication Scenarios

As the development of low-altitude economies and aerial countermeasures continues, the safety of unmanned aerial vehicles becomes increasingly critical, making emitter identification in remote sensing practices more essential. Effective recognition of radio frequency (RF) signal attributes is a prerequisite for identifying emitters. However, due to diverse wireless communication environments, RF signals often face challenges from complex and time-varying wireless channel conditions. These challenges lead to difficulties in data collection and annotation, as well as disparities in data distribution across different communication scenarios. To address this issue, this paper proposes a progressive maximum similarity-based unsupervised domain adaptation (PMS-UDA) method for RF signal attribute recognition. First, we introduce a noise perturbation consistency optimization method to enhance the robustness of the PMS-UDA method under low signal-to-noise conditions. Subsequently, a progressive label alignment training method is proposed, combining sample-level maximum correlation with distribution-level maximum similarity optimization techniques to enhance the similarity of cross-domain features. Finally, a domain adversarial optimization method is employed to extract domain-independent features, reducing the impact of channel scenarios. The experimental results demonstrate that the PMS-UDA method achieves superior recognition performance in automatic modulation recognition and RF fingerprint identification tasks, as well as across both ground-to-ground and air-to-ground scenarios, compared to baseline methods.

Radio frequency fingerprint recognition method based on prior information

The open wireless communication environment is vulnerable to various malicious attacks. Wireless communication hardware devices have unique physical layer characteristics. As an inherent unique feature of wireless signals, radio frequency fingerprints provide a guarantee for the identification and verification of wireless signals. Most of the existing radio frequency fingerprint identification methods only extract fingerprints from one of the steady-state signals or transient signals. Neglecting the connection between the two wireless communication signals results in low identification accuracy of the radio frequency fingerprint identification method under the condition of a low signal-to-noise ratio. Aiming at the respective characteristics of these two signals, a radio frequency fingerprinting method combining transient and steady-state signals based on prior information of wireless signals is proposed. This method combines the characteristic stability of steady-state signals and the integrity characteristics of transient signals, which can effectively identify and classify wireless signals and achieve excellent recognition under low signal-to-noise ratio conditions. The effectiveness of the proposed method is verified by experimental comparison with the traditional radio frequency fingerprinting method on the LFM signal dataset.

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Deep learning (DL) has brought new perspectives and methods to automatic modulation recognition (AMR), enabling AMR systems to operate more efficiently and reliably in modern wireless communication environments through its powerful feature learning and complex pattern recognition capabilities. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are used for sequence recognition tasks, face two main challenges, respectively: the ineffective utilization of global information and slow processing speeds due to sequential operations. To address these issues, this paper introduces CTRNet, a novel automatic modulation recognition network that combines a CNN with Transformer. This combination leverages Transformer’s ability to adequately capture the long-distance dependencies between global sequences and its advantages in sequence modeling, along with the CNN’s capability to extract features from local feature regions of signals. During the data preprocessing stage, the original IQ-modulated signals undergo sliding-window processing. By selecting the appropriate window sizes and strides, multiple subsequences are formed, enabling the network to effectively handle complex modulation patterns. In the embedding module, token vectors are designed to integrate information from multiple samples within each window, enhancing the model’s understanding and modeling ability of global information. In the feedforward neural network, a more effective Bilinear layer is employed for processing to capture the higher-order relationship between input features, thereby enhancing the ability of the model to capture complex patterns. Experiments conducted on the RML2016.10A public dataset demonstrate that compared with the existing algorithms, the proposed algorithm not only exhibits significant advantages in terms of parameter efficiency but also achieves higher recognition accuracy under various signal-to-noise ratio (SNR) conditions. In particular, it performs relatively well in terms of accuracy, precision, recall, and F1-score, with clearer classification of higher-order modulations and notable overall accuracy improvement.

DRTP: A generic Differentiated Reliable Transport Protocol

With rapid development of network communications, the performance requirements of applications are getting more differentiated. Many applications like high-definition video transfer require high throughput but do tolerate occasional packet losses. Traditional generic transport protocols however, only provide inflexible data transfer guarantees (TCP (Transmission Control Protocol) offers full reliability guarantees and UDP (User Datagram Protocol) offers no guarantees). Moreover, TCP pays a significant price to ensure a full reliability guarantee over lossy wireless communications environment like 5G millimeter wave (mmWave) communications. While existing, “partially” reliable transport protocols are either specifically designed for certain applications or need router’s support. In this paper, we design a new generic Differentiated Reliable Transport Protocol (DRTP), aiming to provide a differentiated and deterministic reliability guarantee for upper layer applications while maximizing the throughput under the constraint of guaranteeing a required reliability of data transfer. DRTP is a generic and pure end-to-end partially reliable transport protocol, and as such is easy to deploy regardless of the application in use and with no need for router’s support. The performance of DRTP is evaluated under various network conditions using extensive NS-3 (Network Simulator) simulations and practical experiments over the mmWave communications environment. The results show much higher throughput compared to typical transport protocols while guaranteeing the required transfer reliability.

A miniaturized dual wide‐band polarization reconfigurable antenna integrated with artificial magnetic conductor for next‐generation wireless applications

SummaryIn today's intricate wireless communication environment, ensuring system quality demands the use of a reliable and versatile antenna system. This research article introduces a polarization reconfigurable antenna integrated with a 4 × 4 Artificial Magnetic Conductor (AMC) surface. The AMC unit cell exhibits a triple‐band reflection phase response at 1.8GHz, 4.5GHz, and 5.5GHz, demonstrating double negative metamaterial behavior. The antenna features two distinct C‐shaped metal strips connected to two PIN diodes, enabling dynamic current distribution adjustment. Consequently, the proposed antenna offers three reconfigurable states, facilitating seamless switching between dual circular polarization (left and right‐hand circular polarization) and linear polarization. With a frequency coverage ranging from 1.29 to 2.52GHz and 3.59 to 6.15GHz, the antenna boasts a maximum axial ratio (AR) bandwidth of 31.96%. Additionally, it achieves a maximum peak gain of 5.5 dB and maintains front‐to‐back ratio (FBR) values exceeding 25 dB, while recording a minimum specific absorption rate (SAR) value of 0.1059 W/kg. The integration of the AMC surface ensures enhanced performance of the antenna. Experimental results from constructed prototypes closely align with simulation outcomes, validating the effectiveness of the proposed antenna. Consequently, this antenna holds significant promise for next‐generation wireless applications.

Performance analysis of microstrip patch antenna for wireless communication systems

<p>An antenna may be thought of as a temporary tool that directs radio waves for transmission or reception. Aside from being inexpensive, small, easy to manufacture, and compatible with integrated electronics, the microstrip patch antenna (MPA) offers several other benefits as well. These two methods are often seen as low-cost, adaptable, dependable, high-speed data connection choices that promote user mobility. An overview of how MPA have been used throughout the last several decades is provided in this article. It has been suggested that there are many approaches to enhance the performance of MPA, including the use of composite antennas, highly integrated antenna/array and feeding networks, operating at relatively high frequencies, and using cutting-edge manufacturing methods. Dual or multiband antennas are essential for meeting the demands of wireless services in this rapidly evolving wireless communication environment. Here is an overview of the patch antenna literature for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications.</p>

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.

IDsMA: AN INTEGRATED DIGITAL SIGNATURE AND MUTUAL AUTHENTICATION MECHANISM FOR SECURING THE COGNITIVE RADIO NETWORKS

IDsMA, an Integrated Digital Signature and Mutual Authentication mechanism, is proposed to enhance the security of Cognitive Radio Networks (CRNs). With the proliferation of wireless communication, CRNs face increasing vulnerabilities to unauthorized access and malicious attacks. IDsMA addresses these concerns by offering a robust solution that combines digital signatures and mutual authentication protocols. By employing digital signatures, IDsMA ensures the authenticity and integrity of transmitted data, mitigating the risk of data tampering or forgery. Additionally, mutual authentication protocols establish trust between communication entities, verifying the identities of both the transmitter and receiver. This two-way authentication mechanism enhances the overall security posture of CRNs, safeguarding against unauthorized access and potential threats. IDsMA's integrated approach provides a comprehensive security framework tailored to the dynamic and heterogeneous nature of CRNs, enabling reliable and secure communication in wireless environments. Through its effective deployment, IDsMA aims to foster trust, confidentiality, and integrity in CRNs, facilitating their widespread adoption and utilization in modern wireless networks.

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.

Reconfigurable Intelligent Surfaces for Beamforming in Upcoming Wireless Communications

Reconfigurable Intelligent Surfaces (RIS), also known as Intelligent Reflecting Surfaces (IRS), represent a groundbreaking advancement in wireless communication technology, enabling unprecedented control over electromagnetic (EM) wave propagation. Composed of numerous passive or active elements constructed from metasurfaces, RIS can dynamically alter their electromagnetic responses to optimize signal reflection, refraction, and scattering. These elements, capable of manipulating the phase, amplitude, and polarization of incoming waves, are centrally controlled through sophisticated algorithms that adapt to real-time environmental changes and communication requirements. RIS technology promises significant improvements in wireless communication by extending coverage, enhancing signal quality, managing interference, boosting energy efficiency, and improving security. Additionally, RIS can facilitate precise localization and sensing applications. Despite the high initial implementation costs and the need for complex control mechanisms and infrastructure compatibility, the potential benefits of RIS in creating efficient, secure, and adaptive wireless communication environments underscore its transformative impact on the field.

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.