Wireless Transmission Channel Research Articles

Wireless Channel Propagation Model for Inland Waterway Bridge Scenario

Intelligent shipping is a crucial part of the transportation system, while inland river intelligent shipping is a major safeguard of intelligent transportation. Compared with the studies of mobile fading channels in land-based environments, less current research has focused on channel measurements and modeling for inland waterway bridge environments. In this paper, a segmenting radio channel model is proposed for inland highway and railway combined bridges. The ship's path under the bridge was divided into three phases, and the attenuation of signal strength was modelled separately for each. Hence, it shows ship-to-ship wireless channels in different areas and path loss on inland navigation bridges. A segmented model, instead of a basic path loss model, can accurately forecast path loss and provide a practical approach in ship-to-ship wireless channel transmission scenarios over bridges. Consequently, the channel measurements and modeling in the typical inland waterway are of great significance for establishing a reliable inland navigation broadband radio communication system.

Intelligent-Reflecting-Surface-Assisted Single-Input Single-Output Secure Transmission: A Joint Multiplicative Perturbation and Constructive Reflection Perspective.

Due to the inherent broadcasting nature and openness of wireless transmission channels, wireless communication systems are vulnerable to the eavesdropping of malicious attackers and usually encounter undesirable situations of information leakage. The problem may be more serious when a passive eavesdropping device is directly connected to the transmitter of a single-input single-output (SISO) system. To deal with this urgent situation, a novel IRS-assisted physical-layer secure transmission scheme based on joint transmitter perturbation and IRS reflection (JPR) is proposed, such that the secrecy of wireless SISO systems can be comprehensively guaranteed regardless of whether the reflection-based jamming from the IRS to the eavesdropper is blocked or not. Moreover, to develop a trade-off between the achievable performance and implementation complexity, we propose both element-wise and group-wise reflected perturbation alignment (ERPA/GRPA)-based IRS reflection strategies, respectively. In order to evaluate the achievable performance, we analyze the ergodic secrecy rate (ESR) and secrecy outage probability (SOP) of the SISO secure systems with the ERPA/GRPA-based JPRs, respectively. Finally, by characterizing the simulated and numerical ESR and SOP performance results, our proposed scheme is compared with the benchmark scheme of random phase-based reflection, which strongly demonstrates the effectiveness of our proposed scheme.

Delay-sensitive task offloading and efficient resource allocation in intelligent edge–cloud environments: A discretized differential evolution-based approach

The number of smart wireless devices (WDs) has enormously increased over the last few years due to the advancement of 5G/B5G networks. The advanced applications of such smart WDs, e.g., augmented reality, virtual reality, online gaming, etc., demand excessive resources. Although the WDs are equipped with limited resources, the evolution of edge computing and offloading techniques enables the WDs to offload their resource-intensive tasks to the nearby edge node. These edge nodes might experience higher loads and delays when WDs generate a huge number of tasks. Moreover, the wireless channel bandwidth and transmission data rate of the wireless channels are also limited. Therefore, optimizing the use of available bandwidth as a valuable resource and reducing latency emerge as crucial objectives while offloading tasks. In this paper, a delay-aware resource-constrained offloading problem for edge–cloud systems is mathematically formulated as a 0–1 integer linear programming, and it is shown to be NP-complete. Then, a delay-aware resource-constrained offloading algorithm based on a discretized differential evolution (DARC-DE) is designed. The objectives of the DARC-DE are to maximize the utilization of the resources as bandwidth and minimize the delay. The vectors are efficiently encoded along with the decoding technique. The fitness function is designed by considering execution, offloading, queuing, transmission delay, and bandwidth utilization. The DARC-DE is shown to be executed in polynomial time. To evaluate DARC-DE, extensive simulation is performed in two different scenarios with varying numbers of tasks and edges. Simulation results demonstrate that the proposed DARC-DE can minimize total delay by 15% to 40% in comparison to particle swarm optimization, genetic algorithm, and bees algorithm, respectively. Simulation results also indicate a significant improvement in bandwidth utilization. Taguchi method and alternative average convergence rate are conducted. The statistical tests—analysis of variance, post-hoc, and Friedman tests—are also performed.

Enhancing Industrial Wireless Communication Security Using Deep Learning Architecture-Based Channel Frequency Response

Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.

Equalizer Parameters’ Adjustment Based on an Oversampled Channel Model for OFDM Modulation Systems

A physical model of a wireless transmission channel in the time domain usually consists of the main propagation path and only a few reflections. The reasonable assumptions made about the channel model can improve its parameters’ estimation by a greedy OFDM (Orthogonal Frequency Division Multiplexing) equalizer. The equalizer works flawlessly if delays between propagation paths are in the sampling grid. Otherwise, the channel impulse response loses its compressible characteristic and the number of coefficients to find increases. It is possible to get back to the simple channel model by data oversampling. The paper describes how the above idea helps the OMP (Orthogonal Matching Pursuit) algorithm estimate channel coefficients. The authors analyze the oversampling algorithm on the one hand to assess the influence of filtering function and signal resolution on the quality of the channel impulse response reconstruction. On the other hand, the abilities of the OMP algorithm are analyzed to distinguish components of the oversampled signal. Based on these analyses, we proposed modifications to the compressible channel’s impulse response reconstruction algorithm to minimize the number of transmission errors. A distinction was made between the filters used in the OMP search and channel reconstruction stages before calculating equalizer coefficients. Additionally, the results of the search stage were considered as elements within the groups.

Структура и элементная база приёмо-передающего модуля для организации подводной оптической беспроводной системы передачи данных

In the paper, the results of theoretical study of a wireless optical data transmission channel, as well as experimental study of its characteristics, taking into account different configurations of the optical component base of receiving and transmitting modules, were presented. The model of the wireless underwater optical communication channel with pulse-position modulation (4 bits per symbol) of digital data traffic with 100 Mbps is proposed. The model showed that to organize a communication channel on a 10 m path, the use of an array of light-emitting diodes (LEDs) is requires (the amount is up to 20 LEDs). For this case, a sufficiently large route budget will be provided, even with additional factor that reduce the efficiency of light propagation in seawater. The LEDs were chosen to organize the data transmission channel, since they have a wide radiation pattern and allow the implementation of a relatively simple driver circuit. The required optical radiation power is realized with the use of an array of LEDs. Schematic diagrams of the LEDs driver and power supply for the receiving and transmitting modules have been designed. A measuring layout with optical transmitter and receiver blocks has been implemented. The test signal is a regular sequence of pulses with a duty cycle of 2, and a frequency of 1 MHz. In the receiving module, the signal from the output of the pin photodiode is amplified using a wideband operational amplifier. Sets of 3 pin photodiodes and 10 LEDs were selected and tests were carried out to determine options for optical components that provide the best time characteristics of the output electrical signal in terms of delay time, rising and falling edges of pulses in a wireless optical airborne communication channel. Also, measurements were done on an air path with a length of 0.25-3 m using one or two LEDs as part of the transmitting module. It has been experimentally confirmed that the organization of a communication channel with a length of up to 10 m requires about 15-20 light-emitting diodes. Further study involves the designing and testing of a driver for the LEDs array and a prototype transceiver module.

A Deep Convolutional Autoencoder–Enabled Channel Estimation Method in Intelligent Wireless Communication Systems

Through modeling the characteristics of wireless transmission channels, channel estimation can improve signal detection and demodulation techniques, enhance the spectrum utilization, optimize communication performance, and enhance the quality, reliability, and efficiency of intelligent wireless communication systems. In this paper, we propose a deep convolutional autoencoder–based channel estimation method in intelligent wireless communication systems. At first, the channel time‐frequency response matrix between the transmitter and receiver can be represented as 2D images. Then they are fed into the convolutional autoencoder to learn key channel features. To reduce the structural complexity of the deep learning model and improve its inference efficiency, we adopt the method of removing redundant parameters to achieve model compression. Iterative training and pruning based on stochastic gradient descent (SGD) and weight importance evaluation are alternated to obtain a lightweight deep learning model for channel estimation. Finally, extensive simulation results have verified the effectiveness and superiority of the proposed method.

Cache‐aided multiuser UAV‐MEC networks for smart grid networks: A DDPG approach

AbstractMobile edge computing (MEC) is an important research topic in the field of wireless communication and mobile computing, as it can effectively decrease the latency and energy consumption due to the trade‐off between the communication and computing, where some intensive computing tasks can be offloaded to computational access points (CAPs), especially when the wireless transmission channel is in good condition. This article studies how to intelligently allocate the computing capability and wireless bandwidth among users for a cache‐aided multi‐terminal multi‐CAP MEC network with non‐ideal channel estimation, where there are mobile terminals and CAPs in the network. Each terminal has some tasks that need to be computed in a fast and efficient way. For such a system, we first design the system by jointly considering the computing capability and wireless bandwidth allocation, where the computing and communication delay is used as the performance of metric. To optimize the system performance, we then employ deep deterministic policy gradient to learn an effective strategy on the allocation of computing capability and wireless bandwidth, in order to decrease the system delay as much as possible. Simulations are finally conducted to show the superiority of the proposed studies in this article, especially about the advantages from cache.

Performance enhancement of 5G uplink MIMO over fading-shadowing, path losses, and ISI using LZF equalize

The challenges of the 5G mobile wireless communication systems transmission channels have been changed continuously because of the signal transfer path characteristics such as real-time applications, path loss, multiple users, reflected rays, channel Rayleigh-fading, shadowing, noise, and inter symbol interference (ISI). These challenges have been treated to reach the minimum bit error rate (BER) value of the received data and maximum data rate of high-speed wireless mobile communication systems. In this paper, the linear zero-forcing (LZF) equalizer with uplink multi-input multi-output (MIMO) system has been proposed to enhancement the BER, which is caused by the ISI, Rayleigh fading, shadowing, path losses, and additive white gaussian noise (AWGN). Furthermore, the proposed approach is applied to a different number of antennas constellation to show the effect of increasing the number of antennas on the BER. The results show that the proposed LZF equalizer-MIMO system model has reached lower BER values, and high performance with an increase in the receiver to 8×14, 8×16 and 8×18 antennas constellation at 8, 10, 12, 14, 16, and 18 dB signal to noise ratio (SNR).

RIS Subarray Optimization With Reinforcement Learning for Green Symbiotic Communications in Internet of Things

Symbiotic communications have been deemed as a critical technology for Internet of things (IoT) communications owing to its high spectrum and energy efficiency. Reconfigurable intelligent surface (RIS), which can tune wireless transmission channels by manipulating incident waves through the corresponding electromagnetic elements, is a promising enabler of various symbiotic communications scenarios in IoT. However, when the full electromagnetic elements of RIS are activated, system capacity will be improved and energy efficiency will be reduced inevitably, also with undesirable power consumption. To address this issue, an intelligent dynamic subarray RIS framework based on deep reinforcement learning (DRL) has been proposed. The key idea is to divide RIS electromagnetic elements into several groups and optimize power amplifier factor, independent phase shifts to improve the system energy efficiency under the premise of user’s basic requirements. In particular, we formulate a hybrid optimization problem of RIS subarray partition and beamforming to maximize system energy efficiency. It can be proved that this hybrid optimization is a mixed non-convex integer programming problem. To solve this issue, we proposed a comprehensive DRL framework including two parts, i.e., (1) a Markov decision process (MDP) to model the subarray partition design, amplitude and phase shifts of RIS, and (2) an active RIS subarray optimization scheme based on deep deterministic policy gradient. Numerical results have demonstrated that, compared with the conventional fully-connected RIS, the system energy efficiency can be significantly improved.

Performance Analysis of Adaptive Equalizers Over Multipath Faded Channels: Error Vector Magnitudes

Due to the increasing popularity of digital transmission systems, the need for channel equalizers has been acknowledged. These techniques are designed to counteract the effects of the inter-symbol interference (ISI) caused by the communication channels. An adaptive equalizer is used to operate on the output of a channel in order to provide an approximation of the transmission medium. An adaptive equalizer usually requires a training period to operate successfully. This method eliminates the effects of the wireless transmission channel and allows the subsequent symbol modulation. The paper presents an overview of the various adaptive equalizers, such as the least mean squares (LMS), decision feedback equalizers (DFE), and the Recursive least squares (RLS). It also explores their performance in terms of error vector magnitudes (EVM) over Rician and Rayleigh channels. The paper looks into the effects of adaptive equalizers on various digital modulation techniques for rectangular quadrature phase shift, amplitude modulation, such as the BPSK, QPSK, 4-QAM, 16-QAM, 64-QAM and 256-QAM. These modulations are analysed and measured in terms of symbol error rates and number of incorrect symbols.

The Use of IoT for Determination of Time and Frequency Vibration Characteristics of Industrial Equipment for Condition-Based Maintenance

The subject of study in this article is a method for industrial equipment vibration diagnostics that uses discrete Fourier transform and Allan variance to increase precision and accuracy of industrial equipment vibration diagnostics processes. We propose IoT-oriented solutions based on smart sensors. The primary objectives include validating the practicality of employing platform-oriented technologies for vibro-diagnostics of industrial equipment, creating software and hardware solutions for the IoT platform, and assessing measurement accuracy and precision through the analysis of measurement results in both time and frequency domains. The IoT system architecture for industrial equipment vibration diagnostics consists of three levels. At the autonomous sensor level, vibration acceleration indicators are obtained and transmitted via a BLE digital wireless data transmission channel to the second level, the hub, which is based on a BeagleBone single-board microcomputer. The computing power of BeagleBone is sufficient to work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the state of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is used. The verification of the accuracy and precision of the vibration diagnostics system was carried out on the developed stand. A comparison of the expected and measured results in the frequency and time domains confirms the correct operation of the entire system.

Designing Security for the Sixth Generation: About Necessity, Concepts and Opportunities

Intelligent, comprehensive and, above all, secure wireless interconnection is the driving force behind technological progress. To ensure this, the development towards Sixth Generation (6G) Wireless Systems has been launched and is scheduled to be operational by 2030. This data technology of the future turns 6G into the infrastructure of a new generation of mobile, intelligent, and context-sensitive services, available everywhere and featuring high trustworthiness and performance, relying on both, network-side and off-network context sources. In addition, the networks themselves ought to become intelligent and thus more efficient and resource-saving, which requires a high degree of automated utilization of Artificial Intelligence (AI). Building upon the principles of information and communication theory for both the physical (bit)-transmission layer (PHY) and media access, new communication concepts for 6G will be developed providing the foundations for research into new single and multi-user operation, access and core networks. The flip side of this coin of opportunities: Sophisticated technology inevitably leads to additional security vulnerabilities, open access systems and Open-Radio Access Network (O-RAN) approaches imply new attack vectors. The holistic interconnection of everything renders it ever more attractive to attackers to harm systems, and create damage. Furthermore, enhanced computational power along with quantum computers make conventional systems more vulnerable than ever, and the value of the transmitted data increases tremendously: It is not only machine and sensor data, but also very personal and healthcare data transmitted with 6G. Therefore, the aim is to build a resilient and secure 6G system capable of recognizing attacks and uncertainties, flexibly absorbing them, recovering in a timely and sustainable manner, and compensating for impaired functionality through transformation. This holistic resilience-by-design approach is based, among other things, on technology such as Quantum Key Distribution (QKD) and Post Quantum-Crypto to achieve end-to-end security, Reconfigurable Intelligent Surfaces (RISs) to rely, control and manipulate the wireless transmission channel, Wireless Optical Communication (WOC), Physical Layer Security (PhySec), but also Body Area Networks (BANs), the integration of the human body relying on biometrics and the Tactile Internet (TI). These concepts will be discussed and shed light on in the scope of this work.

Adaptive transmission construction based on polar codes

In actual communication scenarios, wireless transmission channels have the nature of heterogeneity. In order to ensure that data can be transmitted with high reliability and low delay, this paper designs a quasi-optimal polar code that can adapt to various channel environments, which called Q-polar code. Based on the polarization theory of polar codes and the channel state information (CSI), the index set of polar codes is designed and constructed to perform data transmission tasks. The simulation results show that the bit error rate (BER) performance of proposed scheme can be improved compared with traditional polar code, especially when the code length N is small and the code rate is large.

Online Document Transmission and Recognition of Digital Power Grid with Knowledge Graph

Inspired by the ever-developing information technology and scalable information systems, digital smart grid networks with knowledge graph have been widely applied in many practical scenarios, where the online document transmission and recognition plays an important role in wireless environments. In this article, we investigate the online document transmission and recognition of digital power grid with knowledge graph. In particular, we jointly consider the impact of online transmission and recognition based on computing, where the wireless transmission channels and computing capability are randomly varying. For the considered system, we investigate the system performance by deriving the analytical expression of outage probability, defined by the transmission and recognition latency. Finally, we provide some results to verify the proposed studies, and show that the wireless transmission and computing capability both impose a significant impact on the online document transmission and recognition of digital power grid networks.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ БЕЗПРОВОДОВИХ СИСТЕМ ПЕРЕДАЧІ ДАНИХ В УМОВАХ ЗАСТОСУВАННЯ ЗАСОБІВ РАДІОЕЛЕКТРОННОЇ БОРОТЬБИ

The article discusses some of the technologies that ensure the autonomy of unmanned aerial vehicles (UAVs) on the battlefield, methods of increasing the efficiency of UAV communication channels in the context of electronic warfare (EW). The author analyzes the threats to autonomous UAVs that may arise from the use of electronic warfare, and also presents the characteristics of one of the typical modern electronic warfare complexes. This article discusses the development of a test model of a wireless data transmission channel designed to develop technologies for countering electronic warfare and traininga neural network. The use of this model will allow the development of original domestic algorithms for countering enemy electronic warfare. The requirements of manufacturers to the electronic equipment of autonomous UAVs are analyzed. The possibility of using non-professional SDR radio equipment and open source software and hardware platforms for the develop-ment and verification of these countermeasures algorithms is considered. The algorithm for changing data transmission pa-rameters in the event of interference is determined. The choice of the type of neural network for use in the cognitive radio system of the UAV is substantiated. The idea of applying adaptive coding in UAV communication channels using multicompo-nent turbo codes in combination with neural networks that are simultaneously used for cognitive radio was further developed. Using information from the neural network about subsequent changes in the communication channel will reduce the adaptation time of the coding system, which will reduce the number of errors in data transmission and increase the reliability of commu-nication, especially when using dynamic spectrum access.The article is devoted to the problems of creating methods and models that use software-defined radio (SDR), cognitive radio, and multi-level parametric adaptation of turbo-code structures using artificial intelligence to improve the efficiency of wireless data transmission systems in the context of active use of electronic warfare by ensuring the reliability of information

Notice of Removal: A Triggering-and-Encoding Lossless Compression Scheme for Waveform Capturing Rate Enhancement of Android Bluetooth Oscilloscope

Android-based oscilloscopes have been developed for research and education purpose because they would be able to acquire, transmit, display and analyze any electrical signals and be mobile, operable easily. Due to the speed limitation of wireless transmission channel, Android Oscilloscopes have some limited performances such as bandwidth, waveform capturing rate (WCR) and so on. In this paper, we propose a real-time lossless data compression scheme to solve a bottleneck of continuous data flow from DAQ to smartphone. This compression scheme consists of triggering a signal and encoding differences between waveforms. Also, an advanced structure of Android Bluetooth Oscilloscope is proposed to implement the proposed compression algorithm with FPGA-based hardware and software. To evaluate WCR improvement, we firstly analyze relationship between WCR and compression ratio. And then the compression efficiency is verified by the MATLAB simulations using various waveforms such as simple sinusoidal, complex periodic, square, chirp waveforms. We finally study robustness of this instrument in the range of 0~15% noise amplitude and 0~2rad phase offset. All experimental results show that proposed scheme can enhance WCR and also be applied to the wireless transmission fields collecting and displaying electrical waveforms.

Self-Derived Wavelet Compression and Self Matching Reconstruction Algorithm for Environmental Data in Complex Space of Coal Mine Roadway

A crucial assurance for coal mine safety production, prevention and control, and rescue, which is the fundamental tenet of implementing intelligent coal mining, is the safety, stability, and quick transmission of coal mine roadways. However, because of the complex structure of the roadway environment, such as limited and variable space and numerous pieces of equipment, the wireless communication network is affected by the environment, the data transmission channel characteristics are complex and variable, and the existing data transmission methods are weak in adapting to the changing channel. These factors result in poor stability of the transmission of coal mine roadway environment detection data in the wireless communicative network. As a result, this article investigates the wireless communication systems’ real-time transmission in the intricate environmental setting of a coal mine. Based on the application of multiscale wavelet theory in data compression and reconstruction, an adaptive multiscale wavelet compression model based on the wireless data transmission channel is proposed, with an improved Huffman data compression coding algorithm derived from the multiscale wavelet, so that the environmental data meet the wireless communication channel transmission capability. The proposed algorithm boosts the compression ratio and adaptability of environmental data. A self-matching wavelet reconstruction algorithm is developed to achieve real-time and accurate data reconstruction following self-driven wavelet decomposition. The compression and reconstruction experiment performed during real-time wireless transmission of gas concentration data reveals that the original signal’s compression ratio reaches 74% with minor error and high fidelity. The algorithm provides the theoretical foundation for compression and reconstruction in complex coal mine environments for accurate, stable, and real-time data transmission. It is critical for ensuring reliable data transmission in safe production, prevention and control, rescue, and other operations, and it provides theoretical and technical support for intelligent coal mining.

Interval type-2 T-S fuzzy MPC for CPS under hybrid attacks over a multi-channel framework

In this paper, the problem of observer-based model predictive control (MPC) for a multi-channel cyber-physical system (CPS) with uncertainties and hybrid attacks is investigated via interval type-2 Takagi-Sugeno (IT2 T-S) fuzzy model. Both denial-of-service (DoS) and false data injection (FDI) attacks are studied due to the vulnerability of wireless transmission channels. The objective of the addressed problem is to improve the security performance of the multi-channel CPS under malicious attacks, which has not been adequately investigated in the existing MPC algorithms. Moreover, uncertainties which appear not only in the membership functions but in both state and input matrices are considered. In this paper, different from the method that FDI attacks are handled by the bounded functions, an off-line observer is designed to actively defend against the FDI attacks. Meanwhile, an on-line MPC optimization algorithm, which minimizes the upper bound of objective function respecting input constraints, is presented to obtain the secure controller gains. Finally, an illustrative example is provided to verify the effectiveness and superiority of presented approach.

Constructing a Music Network Teaching System by Using Neural Network Model with Wireless Audio Transmission

The purpose is to solve the problems that the traditional teaching methods limit the openness and extension of the music classroom, the interaction between teachers and students, the environment of students’ autonomous learning, and the music teaching situation. Wireless local area networks, Bluetooth, and intelligent transmission channels based on specific frequency can replace wired audio transmission and are widely used in the digital music classroom. Moodle system is used to build a music teaching network system based on the analysis of previous studies and the existing music teaching network platform. The system combines with Convolutional Neural Network (CNN) structure based on cloud computing to effectively identify and create music scores. The system effectiveness is further proved by analyzing the learning effect of the students and teaching effect of teachers in the conservatory of music. The results show that the system makes the experience of teachers and students in the teaching system different from before, and students can freely choose the time and place of class. In addition, the teaching method is flexible, and the teaching methods and resources are real-time. Therefore, in the music teaching network course, it overcomes some shortcomings of the traditional teaching mode. The music class is open and flexible. Teachers and students can have more interactive behavior and realize the students’ self-study and music teaching environment. This exploration can provide a theoretical basis and practical experience for music-related teaching.