Intelligent Wireless Communication Research Articles

Application of machine learning algorithms in resource allocation for wireless communications

Abstract. The accelerated advances of wireless communication technologies in recent years has highlighted the necessity of efficient resource allocation in 5G and upcoming 6G networks, particularly in large-scale, high-density network implementations such as the Internet of Things (IoT) and ultra-dense cellular networks. The application of machine learning offers a number of significant advantages over traditional resource allocation algorithms, including enhanced adaptability, robustness, scalability, and predictive power. Therefore, the paper aims to examine the process of selecting the optimal machine learning algorithm for a specific resource management task. To this end, it provides an overview of the fundamental concepts of machine learning, including deep reinforcement learning (DRL), graph neural networks (GNN), and joint learning. Furthermore, this paper examines the potential applications of machine learning in the field of wireless resource management. The research presented in this paper provides a crucial theoretical foundation and guidance for further exploration and application of machine learning capabilities in the domain of wireless communication resource management. Overall, the research elucidates the potential of machine learning in wireless communication resource management and its applications, thereby advancing knowledge in this field and providing valuable references for the development of efficient and intelligent wireless communication networks.

Rate Optimization of Intelligent Reflecting Surface-Assisted Coal Mine Wireless Communication Systems.

This paper proposes a three-step joint rate optimization method for intelligent reflecting surface (IRS)-assisted coal mine wireless communication systems. Different from terrestrial IRS-assisted communication scenarios, in coal mines, IRSs can be installed flexibly on the tops of rectangular tunnels to address the issues of signals being blocked and interfered with by mining equipment. Therefore, it is necessary to optimize the IRS deployment position, the transmit power and IRS phase shifts to achieve the maximum effective achievable rate at user stations equipped with the proposed system. However, due to the complex channel models of coal mines, the optimization problem of IRS deployment position is non-convex. To solve this problem, two auxiliary variables along with logarithmic operations and Taylor approximation are introduced. On this basis, a three-step joint rate optimization involving the transmit power, IRS phase shifts and IRS deployment position is proposed to maximize the effective achievable rates at the user station. The simulation results show that compared with other rate optimization schemes, the effective achievable rates at the user station using the proposed joint rate optimization scheme can be improved by approximately 12.32% to 54.17% for different parameter configurations. It is also pointed out that the deployment position of the IRS can converge to the same optimal position independent of the initial deployment position. Moreover, we investigate the effects of the roughness of the tunnel walls in a coal mine on the effective achievable rates at the user station, and the simulation results indicate that the proposed three-step joint rate optimization scheme performs better in the coal mine scenario regardless of the roughness.

Federated Multiagent Deep Reinforcement Learning for Intelligent IoT Wireless Communications: Overview and Challenges

Federated Multiagent Deep Reinforcement Learning for Intelligent IoT Wireless Communications: Overview and Challenges

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.

3‐D woven rectangular hollow structure conformal bearing microstrip antenna with different heights: Design, preparation, performance, and simulation

AbstractTraditional microstrip antennas have problems with heavy weight and poor gain. In order to reduce the weight of the antenna itself and improve its gain, the preforms of the 3‐D woven rectangular hollow structure conformal bearing microstrip antenna (3DWRHS‐CB‐MA)with different heights were woven from ultra‐high molecular weight polyethylene (UHMWPE) filament yarns and copper wires on a common loom through reasonable design. The preform was used as the reinforcement and epoxy resin was used as the matrix, four different heights (2, 4, 6, and 8 mm) of 3DWRHS‐CB‐MA were prepared by vacuum‐assisted resin transfer molding (VARTM) process. Finally, the electromagnetic performance was analyzed by high‐frequency structure simulator (HFSS) and experimental testing. The results showed that the frequency deviation of the 2 mm height microstrip antenna was minimal; while the 4 mm height microstrip antenna had better gain efficiency and directional diagram compared to others. The 4 mm height microstrip antenna has good radiation performance with a weight of only 35 g which shows that 3DWRHS‐CB‐MA are potential candidates for future intelligent textile materials and wireless communication fields.Highlights Weaving with UHMWPE filament yarns and copper wires on common looms. The preform was used as the reinforcement, and epoxy resin was used as the matrix. Simulation of electromagnetic characteristics of antennas using HFSS software. Proving the effectiveness of the electromagnetic simulation model. Revealing the electromagnetic radiation mechanisms.

Experiment-based deep learning approach for power allocation with a programmable metasurface

Metasurfaces designed with deep learning approaches have emerged as efficient tools for manipulating electromagnetic waves to achieve beam steering and power allocation objectives. However, the effects of complex environmental factors like obstacle blocking and other unavoidable scattering need to be sufficiently considered for practical applications. In this work, we employ an experiment-based deep learning approach for programmable metasurface design to control powers delivered to specific locations generally with obstacle blocking. Without prior physical knowledge of the complex system, large sets of experimental data can be efficiently collected with a programmable metasurface to train a deep neural network (DNN). The experimental data can inherently incorporate complex factors that are difficult to include if only simulation data are used for training. Moreover, the DNN can be updated by collecting new experimental data on-site to adapt to changes in the environment. Our proposed experiment-based DNN demonstrates significant potential for intelligent wireless communication, imaging, sensing, and quiet-zone control for practical applications.

Retracted: Biobjective UAV/UGV Collaborative Rendezvous Planning in Persistent Intelligent Task-Based Wireless Communication

Retracted: Biobjective UAV/UGV Collaborative Rendezvous Planning in Persistent Intelligent Task-Based Wireless Communication

An endogenous intelligent architecture for wireless communication networks

AbstractThe challenges posed by the future wireless communication network, which will be a huge system with more complex structures, diverse functions, and massive communication ends, will be addressed by intelligent wireless communication technologies. These technologies are playing an increasingly important role in network architecture, computing architecture, resource allocation algorithm design, etc., thanks to the rapid development of artificial intelligence technologies, particularly the deep learning technologies, and their extensive application in various domains. In this paper, an endogenous intelligent architecture is developed to effectively clarify and understand in-depth the relationship among the factors by constructing wireless knowledge graph for the air interface transmission, the core network, as well as the network environment, and so on. Furthermore, the knowledge graph simultaneously reveals the structure and operation mechanism of the whole wireless communication networks. Cause tracing, intelligent optimization, and performance evaluation are sequentially implemented based on the knowledge graph, thus forming a complete closed-loop for endogenous intelligent wireless communication networks.

Retracted: Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

Retracted: Machine Learning-Based Intelligent Wireless Communication System for Solving Real-World Security Issues

Beam‐Steering Metadevices for Intelligent Optical Wireless‐Broadcasting Communications

High‐performance beam‐steering devices play a crucial role for optical wireless communication (OWC), which can meet the demand for increasing number of wireless mobile devices and emerging high‐speed multimedia applications. Conventional beam‐steering optical components like spatial light modulator and digital micromirror device are limited to realize a small steering angle (typ. several degrees), thus dramatically limits the spatial scope of OWC. Herein, a beam‐steering metadevice utilizing an ultracompact metasurface assisted with a spatial light modulator is presented, which can significantly increase the beam‐steering angle without at the cost of complicated optical setup like conventionally used angle magnifier. Based on the actively tunable beam‐steering metadevice, an intelligent bidirectional optical broadcasting communication system is designed and experimentally demonstrated, which exhibits nine broadcasting areas covering a large field‐of‐view of 20° × 20°, with user‐defined dynamic beam‐steering ability in each area, and each user has a data rate of 10 Gbps for both upstream and downstream transmission. The proposed metadevices can meet the demand of intelligent optical wireless communication which requires both high‐performance and low‐cost for practical purpose, under currently available technical architectures.

TS-WHT: A Two-Step Walsh–Hadamard Transform Approach for Blind Error Correcting Code Classification

Blind classification of error-correcting codes is essential for intelligent wireless communication, cognitive radio, and non-cooperative communication. The object of this paper is to investigate the differences in the Walsh spectrum between linear block code, convolutional code, and Turbo code. Based on these differences, a two-step Walsh Hadamard transform (TS-WHT) strategy is proposed for error-correcting code blind classification. Further, according to the detection theory, the peak detection threshold is detailed and optimized with the cyclic feature of CRC codes. The results indicate that when employing the optimized threshold, probabilities of correct classification are superior for linear block codes and convolutional codes. Moreover, with the help of the fault-tolerant feature of the WHT technique, the classification accuracy of the proposed method is substantially better than existing approaches under low signal-to-noise ratio(SNR) conditions.

Double reconfigurable intelligent surface-assisted wireless communication system for energy efficiency improvement over weibull fading channels

Double reconfigurable intelligent surface-assisted wireless communication system for energy efficiency improvement over weibull fading channels

Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network.

Automatic modulation classification (AMC) plays an important role in intelligent wireless communications. With the rapid development of deep learning in recent years, neural network-based automatic modulation classification methods have become increasingly mature. However, the high complexity and large number of parameters of neural networks make them difficult to deploy in scenarios and receiver devices with strict requirements for low latency and storage. Therefore, this paper proposes a lightweight neural network-based AMC framework. To improve classification performance, the framework combines complex convolution with residual networks. To achieve a lightweight design, depthwise separable convolution is used. To compensate for any performance loss resulting from a lightweight design, a hybrid data augmentation scheme is proposed. The simulation results demonstrate that the lightweight AMC framework reduces the number of parameters by approximately 83.34% and the FLOPs by approximately 83.77%, without a degradation in performance.

Theory, Design, and Verification of Miniaturized Phase-Controlled Huygens Antenna for 360° Continuous Beam Scanning

The theory, design, and verification of a novel miniaturized reconfigurable Huygens antenna with 360° continuous beam-scanning capability are demonstrated in this article. Different from the conventional Huygens topology, both the monopole-like Huygens form and the metamaterial loading technique are explored for size reduction. By creatively tuning the direction (or polarization) of the electric dipole (E-dipole) or magnetic dipole (M-dipole), a novel miniaturized pattern reconfigurable Huygens source is first proposed. Then, to control the polarization of the M-dipole [linearly polarized (LP)], a double circularly polarized (CP) wave with a tunable phase difference is introduced. Furthermore, a novel phase-controlled Huygens mechanism is developed by combining the polarization reconfigurable M-dipole and fixed E-dipole. By modulating the feeding phase of the Huygens source, the beam can be steered in any direction in the horizontal plane. The proposed theory and working principle are verified by a metamaterial-loaded tri-mode dielectric resonator radiator and a three-way phase-shifting network. The antenna is operated from 3.40 to 3.68 GHz and is capable of continuously scanning a directional beam from 0° to 360°. The realized peak gain and 3 dB beamwidth are greater than 6.3 dBi and 90°, respectively. This work proposes a new realization method for a simple, low-cost, miniaturized, vertically polarized (VP), 360° continuous beam-steering antenna. It is promising for intelligent wireless communications, the Internet-of-Things (IoT) devices, and DOA estimation applications.

Modeling and Classifying Error-Correcting Codes With Soft Decision-Based HMMs for Intelligent Wireless Communication

The classification of error correcting code (ECC) types is an important task for intelligent wireless communication. Previous solutions to this problem usually ignored the time sequences feature and considered the incoming data as separate code words. In this paper, a novel framework for code type classification is proposed based on the Hidden Markov Model (HMM). The structural differences of different types of ECCs are modeled and analyzed using the transition probability and the emission probability. An HMM-based classifier is proposed to recognize the types of ECCs from the intercepting bitstream. To improve the performance in the noisy transmission environment, the basic classifier is enhanced by soft decision message and relative posterior probability. Simulation results show that the proposed scheme outperforms other existing methods in erroneous conditions and the classification accuracy is associated with the sequence length and decision approach.

Temporal Neural Network Framework Adaptation in Reconfigurable Intelligent Surface-Assisted Wireless Communication

A reconfigurable intelligent surface (RIS) has potential for enhancing the performance of wireless communication. A RIS includes cheap passive elements, and the reflecting of signals can be controlled to a specific location of users. In addition, machine learning (ML) techniques are efficient in solving complex problems without explicit programming. Data-driven approaches are efficient in predicting the nature of any problem and can provide a desirable solution. In this paper, we propose a temporal convolutional network (TCN)-based model for RIS-based wireless communication. The proposed model consists of four TCN layers, one fully connected layer, one ReLU layer, and lastly a classification layer. In the input, we provide data in the form of complex numbers to map a specified label under QPSK and BPSK modulation. We consider 2×2 and 4×4 MIMO communication using one base station and two single-antenna users. We have considered three types of optimizers to evaluate the TCN model. For benchmarking, long short-term memory (LSTM) and without ML are compared. The simulation results are conducted in terms of the bit error rate and symbol error rate which show the effectiveness of the proposed TCN model.

Spectrum Sensing in Cognitive Radio for Internet of Things using Deep Learning Models

An intelligent wireless communication system capable of learning from its surroundings is called cognitive radio. It permits Secondary Users to reuse the radio resources that are made available to them while avoiding damaging interference with licensed users. The key component of cognitive radio technology is spectrum sensing. The literature has become interested in the use of machine learning approaches for spectrum sensing. Enhancement of performance through further spectrum availability forecast and comprehension of the first activities of the primary user. Supporting many SUs at once will boost the speed of spectrum sensing and data transfer over the available, limited spectrum. The performance of current spectrum sensing techniques is constrained by severe channel conditions.With the advent of deep learning in various fields, we are trying to analyze the performance of spectrum sensing (SS) wherein deep learning method is adopted for data fusion. In this research, a data-driven deep learning model is suggested to automatically classify the received raw signal data, which is regarded as time-series data. In this paper, we have provided a comparative analysis of various deep learning models, including ResNet, VGG, LSTM, and MLP. The performance comparison was provided using various sample lengths and SNR values, both low and high. The ResNet model’s performance has produced the highest detection probabilities in both low and high SNR ratios. The simulation result of the proposed methodology would improve the system’s accuracy with a reduction in losses that occurred during the false alarm of prediction as well as an improvement in the probability of detection. Therefore, simulation results of the suggested methodology would lead to an improvement in the system’s accuracy, a decrease in losses from false alarms of prediction, as well as an increase in the likelihood of detection. Better spectrum sensing would be achieved through deep learning’s analysis of PU statistics

Wavefront Control of Millimeter Waves With a VO2-Based Reconfigurable Meta-Reflectarray

In this paper, a novel architecture of a reconfigurable metasurface is proposed and numerically assessed for wavefront control in wireless systems working in the millimeter-wave range. We show that a metasurface made of engineered silicon bricks covered with a thin film of vanadium dioxide provides anomalous reflection with an efficiency of 93% at 60 GHz and a relative bandwidth of 15%. After applying a thermal stimulus that triggers the transition of vanadium dioxide from the insulator phase to the metallic phase, the metasurface abruptly changes its response and provides specular reflection. The device shows high efficiency and broadband operation in both VO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> states, allowing dynamic control of millimeter waves. Therefore, it finds applications in imaging, sensing, and intelligent wireless communications in 5G and 6G systems, especially for beam-shaping and beam-steering. Moreover, the proposed reconfigurable metasurface has a simple configuration that lends itself to standard fabrication tools, and it has a subwavelength size for integrated and compact communication systems.

Performance Analysis of Reconfigurable Intelligent Surface-Assisted Wireless Communication Systems Under Co-Channel Interference

Reconfigurable Intelligent Surface (RIS) can intelligently control the wireless propagation environment by adjusting the signal phase and amplitude in real time, which is considered as one of the key technologies of 6G. Although RIS-assisted wireless communication systems greatly improve transmission efficiency under ideal conditions, there are various uncertainties in actual communication systems. In this paper, RIS-assisted communication system is investigated where the RIS cannot completely eliminate the phase error and the user location is randomly distributed with uncertainty. In addition, there are some interference sources around the user, which are simplified as co-channel interference (CCI). Then we study the performance of RIS-assisted communication system, and derive the closed-form expressions for the outage probability (OP) and channel capacity of the system. Furthermore, we analyze the effects of various parameters on the OP and channel capacity. Finally Monte Carlo simulation is carried out which verify the accuracy of the derivation.

An Amplitude-Modulated Metadevice with Switchable Reflection, Transmission, and Absorption.

In this paper, we propose a reconfigurable metadevice with independent polarization control based on a 90° rotationally symmetric microstructure. Three functionalities of broadband high-efficiency transmission, broadband high-efficiency reflection, and perfect absorption are switched by the on-state and off-state PIN diodes. Coding metadevices designed with diversified lumped element combinations are further studied in detail. By controlling the two diodes on the top layer in opposite states, absorption bandwidth is significantly improved. Reasonable arrangements of coding sequences allow for reflected dual/multi-beam modulation. Electric field distribution, power loss, complex impedance functions, and equivalent circuit models are used to better analyze the physical mechanism of the design. A prototype of the microstructure has been fabricated, and the experimental results agree well with the simulation. Electronic components integrated microstructures with high degrees of freedom have potential applications in intelligent wireless communication, electronic detection, advanced sensors, and smart stealth radomes.