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This paper proposes a high‐frequency multiple‐receiver wireless power transfer (WPT) system with a load‐independent class‐E/F inverter. Each receiver has a post‐regulator, which changes the equivalent resistance seen from the inverter to obtain the necessary power for output voltage regulation. Because the load‐independent class‐E/F inverter generates constant AC current ( CCAC$$ {\mathbf{CC}}_{\mathbf{AC}} $$), the transmitter supplies the minimum required power to the receivers by the change of the equivalent resistances. Besides, the load‐independent inverter consistently achieves zero‐voltage switching (ZVS) without any control. As a result, no information feedback by wireless communication is necessary for output regulation and ZVS achievement, simplifying the system configuration and improving the transient response of the control. This paper presents analytical expressions of the proposed system. Besides, the experiment was carried out with a two‐receiver WPT system. The implemented system worked well by individual and independent output regulation of the post regulator at each receiver. The implemented WPT system achieved the power‐delivery efficiency of 83.4% at the 6.78 MHz transmission frequency and the total output power of 40 W. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is what we have as a great breakthrough in the 5G network. Although the 5G network can support several Internet of Everything (IoE) services, 6G is the network to fully support that. This paper is a survey research presenting the 5G and IoT technology and the challenges coming, with the 6G network being the new alternative network coming to solve these issues and limitations we are facing with 5G. A reference to the Control Plane and User Plane Separation (CUPS) is made with IPv4 and IPv6, addressing which is the foundation of the network slicing for the 5G core network. In comparison to other related papers, we provide in-depth information on how the IoT is going to affect our lives and how this technology is handled as the IoE in the 6G network. Finally, a full reference is made to the 6G network, with its challenges compared to the 5G network. [ABSTRACT FROM AUTHOR]

This paper deals with the design, simulation, and practical modeling of metamaterial-based multiband conformal bandpass filter (BPF) for various wireless communication applications with improved quality factors. The novel metamaterial in the form of a split ring resonator is loaded on the ground plane face of the proposed BPF. The overall dimension of the designed BPF is only 2 8 × 2 8 mm 2 . The proposed BPF is tuned initially for quality factor enhancement based on the thickness of the substrate, physical parameters of the f transmission line, ground plane, externally loaded elements, and the gap in the metamaterial loading. The suggested filter operates at triple band covering the frequency bands from 1.4 to 2.2, 3.6 to 3.9, and 4.8 to 5.9 GHz, which are suitable for sub-6 GHz 5G and other wireless applications. The insertion loss is observed as 1 dB, which is suitable for the proposed BPF. The conformal behavior of the filter is judged through bending deformation analysis at various bending positions like (15 ∘ , 30 ∘ , 45 ∘ , 60 ∘ , and 90 ∘ ). The proposed BPF retains triple pass band characteristics at various bending deformations, which makes it suitable to be used in curved structures or flexible circuitry. The theory of equivalent circuits and quality factor (Q) of the designed BPF is discussed in this paper. The results are analyzed experimentally through ANRITSU-MS2037C combinational analyzer. The proposed BPF is suitable for sub-6 GHz 5G, WLAN, and Wi-Max applications. [ABSTRACT FROM AUTHOR]

In this paper, a complementary split ring resonator (CSRR) loaded compact CPW-fed circular shaped monopole UWB antenna with dual band notch characteristics is designed on an FR-4 substrate. The propounded band-notched UWB antenna is integrated with two frequency selective surface layers referred as bandpass frequency selective surface (FSS-1) and reflector (FSS-2) to offer enhanced radiation performance. Bandpass FSS-1 enhances the gain of the antenna by improving directivity and reflector FSS-2 reflects back the backside radiation of the proposed antenna towards the forward direction thereby increasing gain and directivity of the proposed antenna. Furthermore, a two-element orthogonal MIMO antenna is proposed with improved gain using two FSS layers. The complete configuration of the proposed two-element MIMO antenna consists of CSRR-based radiating patches, monopole ground planes and two integrated FSS layers. The proposed MIMO antenna operates in UWB range from 3.46 GHz to 16.02 GHz with dual notches at 3.9 GHz and 7.2 GHz. Isolation between MIMO elements is greater than 23 dB. Both antennas are fabricated and investigated at 5.9 GHz for vehicular communications. Maximum gain improvements of 8 dB and 6.4 dB are observed in case of the presented single element and MIMO antenna within the entire operating band due to the integration of dual FSS layers. The diversity performance of the suggested MIMO antenna is investigated by analyzing mean effective gain, envelope correlation coefficient, channel capacity loss and diversity gain parameters. The proposed MIMO antenna is a suitable candidate for vehicular and UWB wireless communication applications. [ABSTRACT FROM AUTHOR]

The paper makes a comparison of patch antennas and dipole antennas, two of the most common forms used in wireless communication. Patch antennas are distinguished by their low profile, ease of production and integration with microwave electronics, as opposed to dipole antennas, which have become known for their simplicity, omnidirectional emission patterns and broad application. The discussion begins with an explanation of the basic concepts, design considerations and operational characteristics of patch and dipole antennas. The key performance criteria including bandwidth, gain, radiation efficiency, polarization and impedance matching are assessed using theoretical considerations and actual applications. Case studies from modern wireless applications, such as mobile communication, Wi-Fi networks and Internet of Things (IoT) devices, are used to demonstrate the strengths and limits of each antenna type in real-world circumstances. The comparative review additionally explores advanced strategies for improving the performance of patch and dipole antennas, such as use of metamaterials, reconfigurable components and new substrate materials. Comparative models and experimental findings illustrate the tradeoffs between size, cost and performance, assisting engineers and researchers in determining the best antenna type for certain applications. [ABSTRACT FROM AUTHOR]

With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave band. However, multi-gigabit-per-second throughput and small interference footprint come at the price of adverse signal propagation characteristics, and require a fundamental rethinking of Wi-Fi communication principles. This article describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular, we study the transition from omnidirectional to highly directional communication and its impact on the design of IEEE 802.11ad. [ABSTRACT FROM PUBLISHER]

The concept, performance, and analyses of distinctive, miniaturized metamaterial (MTM) unit cell addressing the forthcoming Sub 6 GHz 5G applications are presented in this paper. Two circular split-ring resonators (CSRR) with two parallel rectangular copper elements in front of the design and a slotted square element in the background make up the suggested metamaterial. It has a line segment with tunable features that is positioned in the center of the little ring copper structure. The suggested design offers a significant operating frequency band of 220 MHz together with a resonance of transmission coefficient S21 at 3.5 GHz. Furthermore, in two (z & x) principal axes of wave propagation, wide-range achievement, single/double-negative (S/DNG) refractive index, negative permittivity, and near-zero permeability properties were demonstrated. Through varying central slotted-strip line length, resonance frequencies can be selectively altered. Moreover, the metamaterial has overall dimensions of 9 × 9 mm2 and is composed on a Rogers 5880 RT substrate. In order to create the suggested MTM's equivalent circuit, which shows similar coefficient of transmission (S21), a proposed design's numerical simulation is carried out in the CST micro-wave studio. This simulation is after that put to comparison with manufacturing of the design. [ABSTRACT FROM AUTHOR]

Within the rapidly evolving landscape of wireless communication technologies, this scientific paper delves into an innovative approach by exploring the integration of the Bluetooth frequency band into a circular antenna design. Leveraging the capabilities of metamaterials. The Bluetooth frequency band a cornerstone for short-range communications, takes center stage in our study. Traditionally associated with wireless connectivity in diverse applications, its integration into a circular antenna structure opens up new avenues for optimizing performance and functionality. Our research aims to not only enhance the efficiency of short-range communication systems but also contribute to the broader discourse on antenna design evolution. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. By scrutinizing the symbiotic relationship between the Bluetooth frequency band and the circular antenna augmented with metamaterial, our study endeavors to push the boundaries of current design paradigms. We believe that this exploration will not only address the contemporary challenges associated with wireless communication but also pave the way for the development of more sophisticated, adaptable, and compact devices in the ever-expanding realm of telecommunications. [ABSTRACT FROM AUTHOR]

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors. [ABSTRACT FROM AUTHOR]

Given the disparity in signal transmission properties for both current bandwidths and millimeter wave radio frequencies, the present propagation models used for low frequency range up to few gigahertz are not suitable to be utilized for the path loss modelling techniques as well as modulation schemes for the high frequency ranges such as millimeter wave (mmWave) spectra. As a result, rigorous research on link analysis as well as path loss modeling are needed to create a broad and suitable transmission scheme with modeling variables that can handle a broad spectrum of mmWave frequency spectra. This paper proposes an improved path loss model for estimating the path loss in an indoor space wireless communication at 28 GHz and 38 GHz frequencies. The test results for the interior non-line-of-sight (NLOS) situations were collected every two meters over spacing of 24 m separating the transmitting and receiving antenna locations to make a comparison the well-known and improved large-scale generic path loss models. The results of the experimental studies obviously demonstrate that the improved propagation model works significantly better than the CI model, owing to its simple setup, precision, and accurate function. The results show that the presented improved model gives better performance. It is observed that the standard deviation of shadow fading can be significantly reduced in the NLOS scenario, implying greater accuracy in predicting the path loss in an indoor environment. [ABSTRACT FROM AUTHOR]

With the popularity of wireless communication and different mobile intelligent terminals, digital education management information systems are becoming increasingly popular among students. The problem of modernized teaching has significantly influenced and altered teaching quality evaluation methodologies and models. However, the findings revealed that the existing intelligent teaching quality diagnostic still needs to improve design and execution. As a result, creating an education management information system is critical, and establishing a digital education management information system has become a critical undertaking. To address these problems, this research paper presents a model for a digital education management information system (DEMIS) that utilizes wireless communication and BP neural networks (BPNNs) to manage digital education in schools. The paper highlights the significance of wireless communication and DEMIS in modern education. The study employs a grey prediction model and a BP neural network model to predict the trend of education development. The DEMIS is set up using MATLAB simulation with management constraint parameters, and the sample data originate from 85 instructors' digital education assessment ratings. The experiment is carried out in two parts: using the grey prediction model and the BPNNs, which verify the suggested method's efficacy in managing digital education in schools. The experimental findings suggest that, by combining wireless communication and BPNNs, the system can handle the campus network's information in a unified manner and give decision assistance to managers, which is very practicable. Therefore, the suggested DEMIS may effectively enhance students' learning experiences and education quality. [ABSTRACT FROM AUTHOR]

A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs. A contemporary review of the utilization of emerging 6G wireless communication for the major applications of smart grids, especially in terms of massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations, are presented in this paper. This article starts with the key enablers of the smart city, along with the necessity of the smart grid for the key enablers of it. The fundamentals of the smart city, smart grid, and 6G wireless communication are also introduced in this paper. Moreover, the motivations to integrate 6G wireless communication with the smart grid system are expressed in this article as well. The relevant literature overview, along with the novelty of this paper, is depicted to bridge the gap of the current research works. We describe the novel technologies of 6G wireless communication to effectively perform the considered smart grid applications. Novel technologies of 6G wireless communication have significantly improved the key performance indicators compared to the prior generation of the wireless communication system. A significant part of this article is the contemporary survey of the considered major applications of a smart grid that is served by 6G. In addition, the anticipated challenges and interesting future research pathways are also discussed explicitly in this article. This article serves as a valuable resource for understanding the potential of 6G wireless communication in advancing smart grid applications and addressing emerging challenges. [ABSTRACT FROM AUTHOR]

Summary: With the popularization of 6G‐driven wireless access networks and the development of edge computing, the connection between piano performance and artificial intelligence algorithms is getting closer and closer. 6G was first used to solve large‐capacity, short‐latency communication problems, and research results in this field abound. However, in practical applications, the deployment of 6G high‐frequency network is a major problem faced by the industry. This paper mainly studies the artificial robust cluster piano demonstration platform from the perspective of the fusion of 6G and edge computing. We firstly consider that the edge computing is a technology in which artificial intelligence algorithms are transplanted to edge devices and chips. The vision of the 6G network is to realize ubiquitous wireless intelligence, which includes three dimensions: mobile ultra‐broadband, super Internet of Things, and artificial intelligence, and realizes the conversion of piano scores into digital signals. Artificial Robust Clustering is a comprehensive technique that incorporates many disciplines, including phonetics, artificial intelligence, computer science, and more. This paper analyzes the fusion technology of 6G and edge computing and studies the robust cluster intelligent piano demonstration platform. The mentioned technologies are combined together to make the designed efficient. [ABSTRACT FROM AUTHOR]

The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is built on a variety of technologies. Wireless communication is an important part of these technologies. In particular, wireless 6G communication can be essential for the growth of the metaverse. In line with the goal of achieving higher rates in the next-generation wireless network for the metaverse, in this paper, a novel conceptualization of intelligent reflecting surface (IRS)-assisted multiple-input single output-based wireless communication in physical world environments is proposed. More specifically, this paper proposes that in the physical world, the IRS-assisted communication between a communication network and users can be reflected in the metaverse through the virtual world (such as digital avatars and the virtual environment). In the simulations, the bit-error rate and spectral efficiency of the receiver terminal were performed and calculated in the metaverse engine for future consideration. [ABSTRACT FROM AUTHOR]

The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes. [ABSTRACT FROM AUTHOR]

Antenna optimization using machine learning is a rapidly evolving field that leverages the power of artificial intelligence to design and improve antenna systems. Antenna optimization is a process of modifying antenna parameters to achieve desired performance metrics, such as gain, bandwidth, radiation pattern, and impedance matching. This paper presents a review of the most advanced development in antenna design and optimization by using machine learning techniques. The aim of this survey is to focus on different machine learning optimization techniques and their optimization capability with efficiency challenges. A deep outline from literature survey on optimization of antennas using machine learning are presented and listing various optimization algorithms and procedures that are applied to produce desired antenna characteristics and specifications. Firstly, a brief introduction of machine learning and its algorithms, later a quick explanation of antenna optimization process followed by an arranged introduction of different types of printed antenna designs using machine learning algorithm are reported. The methods emphasized in this survey have probably an effect on the imminent advancement of antennas for a variety of wireless applications. [ABSTRACT FROM AUTHOR]

In this paper, we explore several widely available software-defined radio (SDR) platforms that could be used for locating with the signal Doppler frequency (SDF) method. In the SDF, location error is closely related to the accuracy of determining the Doppler frequency shift. Therefore, ensuring high frequency stability of the SDR, which is utilized in the location sensor, plays a crucial role. So, we define three device classes based on the measured frequency stability of selected SDRs without and with an external rubidium clock. We estimate the localization accuracy for these classes for two scenarios, i.e., short- and long-range. Using an external frequency standard reduces the location error from 20 km to 30 m or 15 km to 2 m for long- and short-range scenarios, respectively. The obtained simulation results allowed us to choose an SDR with appropriate stability. The studies showed that using an external frequency standard is necessary for minimizing SDR frequency instability in the Doppler effect-based location sensor. Additionally, we review small-size frequency oscillators. For further research, we propose two location sensor systems with small size and weight, low power consumption, and appropriate frequency stability. In our opinion, the SDF location sensor should be based on the bladeRF 2.0 micro xA4 or USRP B200mini-i SDR platform, both with the chip-scale atomic clock CSAC SA.45s, which will allow for minor positioning errors in the radio emitters. [ABSTRACT FROM AUTHOR]

This paper discusses an extended modulation mapping technique to improve hybrid automatic repeat request (ARQ) protocols by reducing communication errors in wireless systems. The extended mapping (EM) hybrid ARQ (HARQ) protocol has been developed using irregular repetition code with turbo processing. This protocol uses EM and ARQ to improve efficiency, including retransmission for error correction. The existing hybrid ARQ technique uses a horizontal iterative decoding scheme to reduce error. The suggested method employs both serial and parallel iterative decoding schemes for error detection and correction. A bit error rate (BER) analysis evaluates the proposed model, EM-HARQ performance in the additive white Gaussian noise channel. The results show a significant gain in signal-to-noise ratio (SNR) from the theoretical limit of the irregular repetition coding using MATLAB simulation software. The adoption of EM in the proposed method improves the SNR to a 0.99 dB performance gap difference and 0.3 dB performance gain due to enhanced error detection. Even with low SNR channels, the proposed EM-HARQ system can achieve a low BER and close to the theoretical limit capacity. [ABSTRACT FROM AUTHOR]

The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology. [ABSTRACT FROM AUTHOR]

This paper proposes a compact dielectric resonator-based high gain dual-band hybrid antenna. The antenna is proposed to work in a dual ISM (industrial, scientific, and medical) band, i.e., at 2.40 - 2.48 GHz and 5.725 - 5.875 GHz. The designed antenna can work as a radiator in the lower band (2.37 - 2.49 GHz) and as a sensor in the upper band (5.34 - 6.00 GHz). The non-invasive blood glucose sensing application is chosen at a higher frequency band, and a lower band is chosen for wireless communication. From a single transmission feed line, both frequency bands are generated by the fundamental mode T M 10 of radiating slot and H E M 11 mode inside cylindrical DRA, respectively. The designed antenna is optimized and simulated to get the best possible results in terms of S-parameter, bandwidth, gain, radiation pattern, efficiency in a lower band, and sensitivity toward blood permittivity in a higher band. This work performs regression analysis and fasting experiments for blood glucose sensing. The link budget analysis is also done at different data rates for wireless communication. The Specific absorption rate (SAR) value is calculated in finger tissue to ensure safety. The simulated and measured results of the designed antenna suggest that it can be used for non-invasive blood glucose sensing and wireless communication applications. To the author's knowledge, no antenna was reported prior for radiating as well as sensing applications. [ABSTRACT FROM AUTHOR]

For multi-user multiple-input-multiple-output (MU-MIMO) system, transmitter utilizes a pre-equalizer based precoding to cancel inter-stream interference for parallel transmission with the aid of accurate feedback of channel state information (CSI) from all receivers. The correct power scaling factor, which normalizes the symbols power of precoding to be a constant one, is required at each receiver. Due to CSI error and limitation of feed-forward link, the received inaccurate power scaling factor will shrink or expand the received constellation points which severely degrades the performance of MIMO system. In this paper, using nonlinear vector perturbation (VP) and linear zero-forcing (ZF) precoding, we analyze the impact of inaccurate power scaling factor on the performance of a MIMO precoding system. The analyzed results show that the mismatched modulo size from inaccurate power scaling factor severely degrades the performance of the VP system, especially for high order M-QAM modulation. In addition, the performance degradation is strongly related with the distribution of representation points for each M-QAM symbol. For linear ZF precoding, the performance loss from the shrinked or expanded constellation points is severe for system using high order M-QAM. In addition, to alleviate the performance degradation, we propose a VP system using partial perturbation points (PPP). By limiting the region of redundant points with vector perturbation for partial M-QAM symbols, the performance degradation due to the inaccurate power scaling factor can be alleviated. [ABSTRACT FROM AUTHOR]

This paper is concerned with mobile coded orthogonal frequency division multiplexing (OFDM) systems. In the high-speed railway wireless communication system, an equalizer or detector should be used to mitigate the intercarrier interference (ICI) and deliver the soft message to the decoder with the soft demapper. In this paper, a Transformer-based detector/demapper is proposed to improve the error performance of the mobile coded OFDM system. The soft modulated symbol probabilities are computed by the Transformer network, and are then used to calculate the mutual information to allocate the code rate. Then, the network computes the codeword soft bit probabilities, which are delivered to the classical belief propagation (BP) decoder. For comparison, a deep neural network (DNN)-based system is also presented. Numerical results show that the Transformer-based coded OFDM system outperforms both the DNN-based and the conventional system. [ABSTRACT FROM AUTHOR]

The rapid growth of the communication frequency band leads to the diversity of receiving frequency bands of smart devices. This paper proposes a reconfigurable wireless superheterodyne receiver for multi-standard communication systems. The receiver is designed through architecture design, chip selection, layout drawing, and physical measurement. The designed receiver consists of receiving module, local oscillator module, and control module. Unlike previous receivers, the proposed receiver selects the frequency channel, local oscillator frequency, and link gain through the control module in the current scene. The control module consists of a single-chip microcomputer and computer. The receiver can work in application scenarios including 2.4–2.5 GHz, 3.3–3.6 GHz, and 4.8–6.0 GHz. This paper discusses the design process of the proposed reconfigurable receiver in detail. Compared with the traditional receiver, the proposed receiver can change its working mode by software and has the characteristics of flexible reconfiguration. In addition, it has the advantages of a large link gain range and good demodulation performance. Moreover, the receiver is low in design cost and easy to manufacture. Therefore, it can be widely used in receiving modules of WLAN and 5G communication systems. [ABSTRACT FROM AUTHOR]

In the process of physical education and training, students' sports injuries are found, and the laws of injury and psychological factors of injury are discovered from them, to propose targeted preventive measures to reduce the incidence of sports injuries. Based on embedded communication technology, it provides a material means for the sharing of information resources. And the application of the microprocessor makes it very useful in physical education. It is hoped that the incidence of track and field sports injuries of physical education students can be reduced, their potential can be tapped, and their sports life span can be prolonged. This paper establishes the evaluation model of sports injury psychology and emotion, and analyzes the influence of sports injury emotion change on injury rehabilitation and psychological state maintenance cycle in Google data analysis platform. 36 undergraduates from 211 universities were recruited as subjects to show them the introduction information of various sports and ask them to complete the questionnaire. This paper adopts the group design method with psychological quality (high / low) as the independent variable and the choice intention of sports (simple sports / complex sports) as the dependent variable. To select suitable sports for young people to carry out psychological experiments and realize the effective control of scene information. According to the score of psychological quality, the group with high score is regarded as young people with high psychological quality, and the group with low score is regarded as young people with low psychological quality. According to the experimental data, the median score of experiment 1 was 5.00. Those who score higher than 5.0 have high psychological quality, while those who score lower than 5.0 have low learning ability. Taking "psychological quality (high / low)" as the independent variable and "sports project (simple sports / complex sports) choice intention" as the dependent variable, this paper carries out one-way ANOVA with the help of spss22.0. The results show that psychological quality has a significant impact on young people's sports injury. [ABSTRACT FROM AUTHOR]

In this paper, an event-driven wireless sensor node is proposed and demonstrated. The primary design objective is to devise a wireless sensor node with miniaturization, integration, and high-accuracy recognition ability. The proposed wireless sensor node integrates two vibration-threshold-triggered energy harvesters that sense and power a threshold voltage control circuit for power management, a microcontroller unit (MCU) for system control, a one-dimensional convolutional neural network (1D-CNN) environment data analysis and vibration events distribution, and a radio frequency (RF) digital baseband transmitter with IEEE 802.15.4-/.6 protocols. The dimensions of the wireless sensor node are 4 × 2 × 1 cm3. Finally, the proposed wireless sensor node was fabricated and tested. The alarming time for detecting the vibration event is less than 6 s. The measured recognition accuracy of three events (knock, shake, and heat) is over 97.5%. The experimental results showed that the proposed integrated wireless sensor node is very suitable for wireless environmental monitoring systems. [ABSTRACT FROM AUTHOR]

With the advancement of network security, intrusion detection system (IDS) is increasingly used for network-connected environments. As the work of enterprises, governments, and other organizations has increasingly relied on computer network systems, protecting these systems from attacks has become a top priority. IDS has become an essential tool for safeguarding the systems with the increasing number of connected devices. To address the shortcomings of existing IDS, this research proposes an Enterprise Network for Intrusion Detection System (ENIDS) with a fast localization algorithm for cloud-based infrastructure. The proposed system detects and locates attacks by identifying abnormal domain values in the header of packets at the data link layer, network layer, and transport layer. ENIDS comprises three components: an event generator that serves as the source of event record flow, an analysis engine that checks if an attack has occurred based on the information sent by the event generator, and a reaction component that generates a response based on the results of the analysis engine. Additionally, this paper explains the fast localization model of intrusion detection for data of enterprises by explaining keyword selection methods. Experimental results show that the proposed method has a higher localization rate in comparison to direct localization, with a localization rate of 95.7% for the static targets and 92.7% for the dynamic targets. ENIDS has also been compared to existing systems using Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The proposed method has the highest accuracy (96.25%), precision (95.57%), recall (92.24%), and F1-score (93.57%). The simulation results show that the model is effective and can detect and locate the data intrusion behavior quickly. [ABSTRACT FROM AUTHOR]

Wireless communication and Deep Learning can enhance the capabilities of an educational information management system by optimizing physical resources, improving student learning outcomes, and reducing costs. However, the current information asymmetry in educational information management makes providing convenient information services for schools challenging. Therefore, this paper proposes an educational information management system (EIMS) that integrates wireless communication and deep learning techniques to optimize physical resources, improve student learning outcomes, and reduce costs. The current information asymmetry in educational information management makes providing convenient information services for schools challenging. The suggested design of the EIMS consists of four components: terminals, subsystems, server and database, and school website. The client/server and the browser/server modes are adopted to cross and utilize them under the work features of educational information management. The functional design of the system consists of a management terminal, a teacher terminal, and login and registration. The management terminal includes seven module functions: personal management function, comprehensive query function, status statistics function, attendance management function, teaching resource management function, and basic data management function. The teacher terminal is mainly divided into two parts, one is to upload the video information of the teaching status of the course, and the other is the docking of the algorithm. The system database design consists of four modules: student status management, training management, degree management, and course selection management. In addition, the designed forms include student information, teacher personal information, class schedule, cultivation plan, and student attendance sheet. The suggested EIMS is experimentally tested and analyzed using MATLAB software to educational information management system under BP neural network. The experimental results show that the BP neural network has good applicability to the educational information management system and can accurately predict the trend of educational information management, providing a reference for further improvement. [ABSTRACT FROM AUTHOR]

Summary: Wireless multimodal interactions over long distance (WMILD) would give rise to numerous thrilling applications, such as remote touching and immersive teleoperations. However, long distances can induce large propagation delays, which makes it difficult to meet the ultra‐low latency requirements in haptic‐visual interactions. Considering existing works mainly focused on the wireless access part, this paper designs an end‐to‐end framework for general WMILD applications based on the digital twin (DT) technology and proposes an intelligent resource allocation and parameter compression scheme to guarantee WMILD performance under constraint network resources. In the framework, user device can acquire real‐time remote interactions by performing local interactions with nearby base station (BS), where a DT of the remote side is deployed to predict the remote haptic‐visual feedbacks. A reliable DT updating process is carefully designed to guarantee the DT accurately model its dynamic physical counterpart. To optimize the updating reliability, we formulate the resource allocation and parameter compression to be a constraint‐Markov decision problem, under the constraints on energy consumption, multimodal interactions and updating latencies. Then, a safe deep reinforcement learning algorithm is proposed to adapt resources and compression according to the dynamic DT updating workload, multimodal data‐streams and remote transmission capacities. Simulation shows the framework can achieve high updating reliability compared with baselines. [ABSTRACT FROM AUTHOR]

In order to improve the detection accuracy of the collision detection system for unmanned ships at sea, this paper proposes a dynamic collision detection method based on improved ZigBee network wireless communication technology. The collision parameter detection model of unmanned ships at sea based on the ZigBee network and wireless communication network control is constructed. The communication network model is established by using wireless sensor network communication and radio frequency identification tag recognition technology. The experimental results show that the system can more accurately analyze and detect the collision of unmanned ships at sea and judge the route information of ships when there are obstacles. The collision dynamic detection accuracy of the system is 25% higher than that of traditional methods on average, and the performance is superior. [ABSTRACT FROM AUTHOR]

Modern infrastructure heavily relies on robust concrete structures, underscoring the critical need for effective monitoring to ensure their safety and durability. This paper addresses this imperative issue by introducing an innovative automated and wireless system for continuous structural monitoring. By employing embedded electrical resistivity sensors coupled with a wireless-based data transmission mechanism, real-time data collection becomes feasible. We provide a general description of the system's architecture and its application in a pilot study covering the effects of the devices on concrete properties and data transmission. The dielectric properties of concrete specimens were investigated under natural and accelerated curing/degradation and the results were used in the final design of the antenna device. Furthermore, a pilot test comprising four reinforced concrete columns was used to investigate the range of data transmission from inside to outside of the concrete, the effects of the hardware device on the compressive strength and concrete distribution in the columns, and the data transmission quality in real time under realistic exposure conditions. [ABSTRACT FROM AUTHOR]

In wireless communication, to fully utilize the spectrum and energy efficiency of the system, it is necessary to obtain the channel state information (CSI) of the link. However, in Frequency Division Duplexing (FDD) systems, CSI feedback wastes part of the spectrum resources. In order to save spectrum resources, the CSI needs to be compressed. However, many current deep-learning algorithms have complex structures and a large number of model parameters. When the computational and storage resources are limited, the large number of model parameters will decrease the accuracy of CSI feedback, which cannot meet the application requirements. In this paper, we propose a neural network-based CSI feedback model, Mix_Multi_TransNet, which considers both the spatial characteristics and temporal sequence of the channel, aiming to provide higher feedback accuracy while reducing the number of model parameters. Through experiments, it is found that Mix_Multi_TransNet achieves higher accuracy than the traditional CSI feedback network in both indoor and outdoor scenes. In the indoor scene, the NMSE gains of Mix_Multi_TransNet are 4.06 dB, 4.92 dB, 4.82 dB, and 6.47 dB for compression ratio η = 1/8, 1/16, 1/32, 1/64, respectively. In the outdoor scene, the NMSE gains of Mix_Multi_TransNet are 3.63 dB, 6.24 dB, 4.71 dB, 4.60 dB, and 2.93 dB for compression ratio η = 1/4, 1/8, 1/16, 1/32, 1/64, respectively. [ABSTRACT FROM AUTHOR]

Soil moisture is a key parameter in determining crop growth and yield. Modern agriculture does not only take into account soil moisture measurements obtained once at selected points in the field, but also focuses on moisture monitoring. Knowledge of the variability over time and analysis of the variation in readings caused by heavy rainfall or strong sunshine and high soil temperature changes is very important for future modelling, prediction and automated irrigation. The aim of this paper is to present a developed soil moisture monitoring system for a soil profile up to 34 cm depth incorporating Internet of Things technologies. The system was built based on an eight-channel time domain reflectometer to measure soil moisture based on dielectric properties. The developed system integrates both dielectric moisture measurement and innovative web-based server and visualisation services. The autonomous station was equipped with a long-range wireless communication and a solar-charged battery. Analysis of the results enabled us to observe the subtle deviations of moisture content of the upper soil layer during diurnal cycles. The designed station can be used in the future to integrate a high accuracy automatic field irrigation system and acquire data for precision agriculture. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive overview of the microgrid (MG) concept, including its definitions, challenges, advantages, components, structures, communication systems, and control methods, focusing on low-bandwidth (LB), wireless (WL), and wired control approaches. Generally, an MG is a small-scale power grid comprising local/common loads, energy storage devices, and distributed energy resources (DERs), operating in both islanded and grid-tied modes. MGs are instrumental to current and future electricity network development, such as a smart grid, as they can offer numerous benefits, such as enhanced network stability and reliability, increased efficiency, an increased integration of clean and renewable energies into the system, enhanced power quality, and so forth, to the increasingly growing and complicated power systems. By considering several objectives in both islanded and grid-tied modes, the development of efficient control systems for different kinds of MGs has been investigated in recent years. Among these control methods, LB communication (LBcom)-based control methods have attracted much attention due to their low expenses, recent developments, and high stability. This paper aims to shed some light on different aspects, a literature review, and research gaps of MGs, especially in the field of their control layers, concentrating on LBcom-based control methods. [ABSTRACT FROM AUTHOR]

Dedicated fieldbuses were developed to provide temporal determinisms for industrial distributed real-time systems. In the early stages, communication systems were dedicated to a single protocol and generally supported a single service. Industrial Ethernet, which is used today, supports many concurrent services, but usually only one real-time protocol at a time. However, shop-floor communication must support a range of different traffic from messages with strict real-time requirements such as time-driven messages with process data and event-driven security messages to diagnostic messages that have more relaxed temporal requirements. Thus, it is necessary to combine different real-time protocols into one communication network. This raises many challenges, especially when the goal is to use wireless communication. There is no research work on that area and this paper attempts to fill in that gap. It is a result of some experiments that were conducted while connecting a Collaborative Robot CoBotAGV with a production station for which two real-time protocols, Profinet and OPC UA, had to be combined into one wireless network interface. The first protocol was for the exchange of processing data, while the latter integrated the vehicle with Manufacturing Execution System (MES) and Transport Management System (TMS). The paper presents the real-time capabilities of such a combination—an achievable communication cycle and jitter. [ABSTRACT FROM AUTHOR]

In the power line communication network, the electrical node switch connected to the Power Line Communication network will affect the impedance, noise and signal attenuation of the network. The connection change between the network nodes will lead to the change of the topology structure. This paper proposes a new type of automatic modulation pattern recognition system assisted by blind channel recognition based on a convolutional neural network, which is composed of two convolutional neural networks with different architectures, and successfully improves the recognition accuracy of the traditional automatic modulation pattern recognition system. The Signal to Noise Ratio recognition rate of the proposed blind channel recognition assisted Adaptive Multi-Rate system is as high as 99 %, which is more than 5 % higher than that of other comparison methods. After several iterations, the accuracy and loss function of the model studied in this paper are still at a stable level. The intelligent recognition technology of blind signals proposed in this paper can effectively improve the interference problem of communication signals in power system and has a promotion effect in other communication systems, which can achieve higher technical application value. [ABSTRACT FROM AUTHOR]

Aiming at multi-robot online evacuation from unknown polygon, this paper proposed the problem of multirobot online evacuation from a regular hexagon based on the original evacuation model（triangle, square, circle）. 2 k（ k≥1） robots with wireless communication start from the same starting point, search for the exit on the regular hexagon boundary cooperatively and evacuate. The evacuation cost is the time it takes for all robots to be evacuated from the exit. This paper analyzed the efficiency of the algorithm using evacuation overhead. According to the number of robots and the initial position, when k=1, robots are initially at the regular hexagonal boundary or inside; when k>1, robots are initially at the regular hexagon boundary. This paper proposed respectively efficient online algorithms of multirobot evacuation for the above situations. It proves that increasing the number of robots has a positive significance in reducing the algorithm overhead when the starting point of the robots is the same. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasingly applied to surveillance tasks, thanks to their excellent mobility and flexibility. Different from existing works using UAVs for video surveillance, this paper employs a UAV team to carry out collaborative radio surveillance on ground moving nodes and disguise the purpose of surveillance. We consider two aspects of disguise. The first is that the UAVs do not communicate with each other (or the ground nodes can notice), and each UAV plans its trajectory in a decentralized way. The other aspect of disguise is that the UAVs avoid being noticed by the nodes for which a metric quantifying the disguising performance is adopted. We present a new decentralized method for the online trajectory planning of the UAVs, which maximizes the disguising metric while maintaining uninterrupted surveillance and avoiding UAV collisions. Based on the model predictive control (MPC) technique, our method allows each UAV to separately estimate the locations of the UAVs and the ground nodes, and decide its trajectory accordingly. The impact of potential estimation errors is mitigated by incorporating the error bounds into the online trajectory planning, hence achieving a robust control of the trajectories. Computer-based simulation results demonstrate that the developed strategy ensures the surveillance requirement without losing disguising performance, and outperforms existing alternatives. Note to Practitioners—The paper is motivated by the covertness requirement in the radio surveillance (also called eavesdropping) by UAVs. In some situations, the UAV user (such as the police department) wishes to disguise the surveillance intention from the targets, and the trajectories of UAVs play a significant role in the disguising. However, the typical UAV trajectories such as standoff tracking and orbiting can easily be noticed by the targets. Considering this gap, we focus on how to plan the UAVs’ trajectories so that they are less noticeable while conducting effective eavesdropping. We formulate a path planning problem aiming at maximizing a disguising metric, which measures the magnitude of the relative position change between a UAV and a target. A decentralized method is proposed for the online trajectory planning of the UAVs based on MPC, and its robust version is also presented to account for the uncertainty in the estimation and prediction of the nodes’ states. [ABSTRACT FROM AUTHOR]

Sensory data is only meaningful if correctly associated with originating locations, e.g., in three-dimensional wireless sensor systems inside an aircraft cabin. Received signal strength is a cost-effective option to locate low-cost sensors due to its universal availability, but suffers from coarse ranging accuracy with multiplicative errors. This paper presents a new received signal strength-based approach to locating a large number of low-cost sensors given their possible three-dimensional installation points. Our approach achieves the accuracy which has not been achieved in the literature. The approach first cooperatively locates the sensors in the continuous three-dimensional spaces, then associates the continuous location estimates to the installation points, and finally refines the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous three-dimensional spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local three-dimensional regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated experimentally with signal strength measurements collected in a Fokker 100 passenger plane, achieves 100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly with much shorter execution time. Note to Practitioners—This paper is motivated by a goal to realize various aircraft automation applications through three-dimensional localization and association of wireless sensors. For example, automatic identification of seat locations can be implemented efficiently with wireless sensors, which enables effortless re-association between control buttons in the seats and the corresponding functions after cabin refurbishment. Meanwhile, automatic detection of missing/misplaced safety equipment can be carried out to improve the efficiency of flight preparation. There currently lacks an received signal strength-based three-dimensional sensor association method in indoor environments. The theoretical framework proposed in this paper aims to utilize the readily available received signal strength measurements to associate Commercial-Off-The-Shelf sensors to their possible installation points. This approach avoids the requirement of labor-intensive fingerprinting effort and high-cost equipment. The proposed approach has been tested and evaluated using both synthetic data and real data collected in a passenger plane as a proof of concept. [ABSTRACT FROM AUTHOR]

The development and growth of Wireless Sensor Networks (WSNs) is significantly propelled by advances in Radio Frequency (RF) and Visible Light Communication (VLC) technologies. This paper endeavors to present a comprehensive review of the state-of-the-art in cognitive hybrid RF-VLC systems for WSNs, emphasizing the critical task of seamlessly integrating Cognitive Radio Sensor Networks (CRSNs) and VLC technologies. The central challenge addressed is the intricate landscape of this integration, characterized by notable trade-offs between performance and complexity, which escalate with the addition of more devices and increased data rates. This scenario necessitates the development of advanced cognitive radio strategies, potentially facilitated by Machine Learning (ML) and Deep Learning (DL) approaches, albeit introducing new complexities such as the necessity for pre-training with extensive datasets. The review scrutinizes the fundamental aspects of CRSNs and VLC, spotlighting key areas like Energy Efficient Resource Allocation, Industrial Scenarios, and Energy Harvesting, and explores the synergistic amalgamation of these technologies as a promising pathway for enhanced spectrum utilization and network performance. By delving into the integration of cognitive radio technology with visible light, this study furnishes valuable insights into the potential for innovative applications in wireless communication, presenting a balanced overview of the current advancements and prospective avenues in the field of cognitive hybrid RF/VLC systems. [ABSTRACT FROM AUTHOR]

The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4 Q 1 Q 2 + 8 Q 1 − 5 , when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation. [ABSTRACT FROM AUTHOR]

This paper concerns the problem of defending against spoofing attacks without a secret key. We address the problem using the Physical-Layer-Authentication (PLA) because of its high security, low overhead, and high compatibility. However, many PLA schemes have the following limitations: quantization errors, local optimums, and performance loss due to the change in the communication environment. In this paper, two phase-noise-based PLA schemes are proposed to address the limitations of the prior schemes. We denote the first scheme as the Multiple Phase Noises PLA (MPP) scheme, which realizes the PLA by using multiple phase noise innovations. Note that since the MPP scheme avoids using any quantization algorithm, it outperforms the prior schemes on the authentication performance. We denote the second scheme as the Enhanced Multiple Phase Noises PLA (EMPP) scheme, which introduces an artificial random phase to the transmitted symbols at the transmitter to further improve the authentication performance. The theoretical analyses of the proposed schemes over fading channels are provided, where the closed-form expressions are derived. Theoretical comparisons between the proposed schemes and prior schemes are provided. The theoretical analyses and simulation results demonstrated the superiority of the proposed schemes. In comparison with the prior schemes, the MPP scheme achieves 13% authentication-performance gain without demodulation-performance loss, while the EMPP scheme achieves 42% authentication-performance gain with merely 16% demodulation-performance loss. [ABSTRACT FROM AUTHOR]

This paper studies the capacity of the two-user intensity-modulation/direct-detection (IM/DD) interference channel (IC), which is relevant in the context of multi-user optical wireless communications. Despite some known single-letter capacity characterizations for general discrete-memoryless ICs, a computable capacity expression for the IM/DD IC is missing. In this paper, we provide tight and easily computable inner and outer bounds for a general two-user IM/DD IC under peak and average optical intensity constraints. The bounds enable characterizing the asymptotic sum-rate capacity in the strong and weak interference regimes, as well as the generalized degrees of freedom (GDoF) in the symmetric case. Using the obtained bounds, the GDoF of the IM/DD IC is shown to have a ‘W’ shape similar to the Gaussian IC with power constraints. The obtained bounds are also evaluated numerically in different interference regimes to show their tightness, and used to study the performance of on-chip and indoor OWC systems. [ABSTRACT FROM AUTHOR]

To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not be directly transmitted from the track-side BS. In this paper, we propose an UAV-assisted scheduling scheme which utilizes a UAV to serve as relay for such flows. The proposed scheme also utilize two mmWave bands, one for the BS links and the other for the UAV links. The proposed algorithm aims to maximize the number of flows with their QoS requirements satisfied. Simulations demonstrate that the proposed scheme achieves a superior performance on the number of completed flows and the system throughput over two baseline schemes. [ABSTRACT FROM AUTHOR]

Online manipulation of multiple micro- and nanoscale agents is of major interest for various research applications. Among the biggest limitations of wireless external actuation are its global and coupled influences in the workspace, which limit the robust manipulation of multiple agents independently and simultaneously. In this paper, we propose novel motion planning algorithms, $\tt Bi$ - $\tt iSST$ and $\tt Ref$ - $\tt iSST$ , to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Both algorithms are extended by the stable sparse rapidly-exploring random tree kinodynamic motion planning algorithm. The $\tt Bi$ - $\tt iSST$ uses a bidirectional approach to speed up the searching process. A novel connection process is proposed to connect the two trees efficiently by applying an optimization procedure. The $\tt Ref$ - $\tt iSST$ uses the workspace information to quickly generate global-routing trajectories as references, then guides the search process more effectively by getting more accurate heuristics according to the reference global-routing trajectories. A transition matrix similar to that in Markov Decision Processes is used to form the reference trajectory. Compared with the state-of-the-art $\tt iSST$ algorithm, the proposed algorithms quickly update feasible solutions and converge to a near-optimal, minimum-time solution to increase the efficiency of the simultaneous manipulation of multiple micro agents using global external fields. Extensive analysis and physical experiments are presented to confirm the effectiveness and the performance of the motion planning algorithms. Note to Practitioners—Autonomous manipulation of multiple micro- and nanoscale agents is of major interest for various research applications. Wireless actuation is a promising way to position those objects. The commonly used non-contact actuation techniques include magnetic actuation, electrical field actuation, optical tweezers, and actuated flows, etc. Among the biggest limitations of wireless external actuation are its global and coupled influences in the workspace, which limit the capability to robustly manipulate multiple agents independently and simultaneously. In this paper, we propose novel bidirectional informed sampling-based motion planning algorithms to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Novel heuristics and informed reference trajectory are used to guide the search for manipulating multiple agents under global fields. Numerical simulations and physical experiments are presented to demonstrate the performance of the motion planning design. The proposed algorithm guarantees anytime performance and quickly converges to a near-optimal minimum time solution for multiple agents. Although we focus on the electric-field actuated multiple-micro-agent system, the proposed motion planning strategies are not limited to the actuation and can be generalized to other field-based applications, in which the actuation among a group of multiple agents are coupled or intertwined. [ABSTRACT FROM AUTHOR]

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes. [ABSTRACT FROM AUTHOR]

Coded caching is a promising technique to smooth out network traffic by storing part of the library content at the users’ local caches. The seminal work on coded caching for single file retrieval by Maddah-Ali and Niesen (MAN) showed the existence of a global caching gain that scales with the total memory in the system, in addition to the known local caching gain in uncoded systems. This paper formulates a novel cache-aided matrix multiplication retrieval problem, relevant for data analytics and machine learning applications. In the considered problem, each cache-aided user requests the product of two matrices from the library. A structure-agnostic solution is to treat each possible matrix product as an independent file and use the MAN coded caching scheme for single file retrieval. This paper proposes two structure-aware schemes, which partition each matrix in the library by either rows or columns and let a subset of users cache some sub-matrices, that improve on the structure-agnostic scheme. For the case where the library matrices are “fat” matrices, the structure-aware row-partition scheme is shown to be order optimal under some constraint. [ABSTRACT FROM AUTHOR]