This hardware paper introduces an experimental testbed for in-body optical wireless communication (OWC) studies. The conventional version often relies on bulky optical benches and costly supporting equipment, which are often cost-prohibitive for many research institutions. The proposed testbed featured a small footprint, lightweight, a vertically aligned optical path (with fixed optical component placement), and ambient light shielding. It can be printed using commercial 3D printing, reducing costs compared to conventional optical benches. The 3D-printable testbed consists of a box-like chassis that securely positions a near-infrared (NIR) LED TX at the top and a photodetector RX at the bottom, with a tissue sample (e.g., ex-vivo porcine tissue or a tissue-mimicking phantom) held firmly in between. All design files, including CAD and STL formats, along with detailed assembly instructions, are made openly available. The inherent design structure enables faster alignment, and the shields can effectively protect against exposure to indoor ambient light (e.g., typical laboratory lighting), thereby improving experimental reliability. The modular nature of the testbed allows for easy customization to accommodate sensors of different wavelengths and different tissue models. The proposed testbed offers practical benefits and an accessible solution for researchers conducting in-body OWC studies, especially when access to high-end optical equipment is limited.
Favorable neighboring interactions and economical transmission costs are the foundations of formation-containment control (FCC), while the complex marine environments hamper its expansion on networked unmanned surface vehicles (USVs). In this context, this paper investigates an intermittent dynamic event-triggered control scheme for USVs experiencing communication interruptions to achieve FCC. Specifically, the control architecture consists of two synchronously working sub-layers. In the first layer, an intermittent communications-based formation tracking controller is initially developed to endow USVs with higher endurance against communication interruptions, such that the leader USVs can form a desired formation pattern while following a virtual leader. Meanwhile, a dynamic event-triggered mechanism (DETM) is incorporated into the intermittent controller to reduce the update frequency of control signals with computable minimum inter-event time (MIET). Similarly, an intermittent DETM-based controller is proposed for followers to achieve containment missions in the second layer. Moreover, the global information is unnecessary with time-varying control gains. Finally, the simulations are provided to verify the effectiveness and superiority of the proposed control scheme.
Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments.
Bluetooth Low Energy (BLE) is a prominent short-range wireless communication protocol widely extended for communications and sensor systems in consumer electronics and industrial applications, ranging from manufacturing to retail and healthcare. The BLE protocol provides four generic access profile (GAP) roles when it is used in its low-energy version, i.e., ver. 4 and beyond. GAP roles control connections and allow BLE devices to interoperate each other. They are defined by the Bluetooth special interest group (SIG) and are primarily oriented to connect peripherals wirelessly to smartphones, laptops, and desktops. Consequently, the existing GAP roles have characteristics that do not fit well with vehicular communications in cooperative intelligent transport systems (C-ITS), where low-latency communications in high-density environments with stringent security demands are required. This work addresses this gap by developing two new GAP roles, defined at the application layer to meet the specific requirements of vehicular communications, and by providing a service application programming interface (API) for developers of vehicle-to-everything (V2X) applications. We have named this new approach ITS-BLE. These GAP roles are intended to facilitate BLE-based solutions for real-world scenarios on roads, such as detecting road traffic signs or exchanging information at toll booths. We have developed a prototype able to work indistinctly as a unidirectional or bidirectional communication device, depending on the use case. To solve security risks in the exchange of personal data, BLE data packets, here called packet data units (PDU), are encrypted or signed to guarantee either privacy when sharing sensitive data or authenticity when avoiding spoofing, respectively. Measurements taken and their later evaluation demonstrated the feasibility of a V2X BLE network consisting of picocells with a radius of about 200 m.
This paper presents a novel Double-negative (DNG) metamaterial (MM) resonator with a mirror-symmetric configuration, designed to exhibit multiband resonances in the S, C, and X bands. The resonator is fabricated using advanced processing techniques on a Rogers 5880 substrate and features electrodeposited copper. It consists of four equal regions, each containing interconnected split-ring resonators that are connected through a cross-shaped structure to ensure mirror symmetry. The simulation results demonstrate six resonance points at frequencies of 2.45 GHz, 4.27 GHz, 6.86 GHz, 8.98 GHz, 10.69 GHz, and 11.65 GHz. These resonances are characterized by near-zero/negative permeability, negative permittivity, refractive index, and impedance. Furthermore, the cross-polarization effect of incident waves is investigated. Additionally, the potential for tunability of resonance frequencies is explored through a sandwiched configuration of the MM resonator, achieved by modifying the cover of the resonating patch. Moreover, the equivalent circuit model of the proposed MM resonator is in good agreement with practical measurements, validating the simulation results. The new tuning method for MM resonators holds the promise for future sensing applications and wireless communications.
With the increasing demand for the use of technology in all matters of daily life and business, the demand has increased dramatically to transform business electronically especially regards COVID-19. The Internet of Things (IoT) has greatly helped in accomplishing tasks. For example, at a high temperature, it would be possible to switch on the air conditioner using a personal mobile device while the person is in the car. The Internet of Things (IoT) eases lots of tasks. A wireless sensor network is an example of IoT. Wireless sensor network (WSN) is an infrastructure less self-configured that can monitor environmental conditions such as vibration, temperature, wind speed, sound, pressure, and vital signs. Thus, WSNs can occur in many fields. Smart homes give a good example of that. The security concern is important, and it is an essential requirement to ensure secure data. Different attacks and privacy concerns can affect the data. Authentication is the first defence line against threats and attacks. This study proposed a new protocol based on using four factors of authentication to improve the security level in WSN to secure communications. The simulation results prove the strength of the proposed method which reflects the importance of the usage of such protocol in authentication areas.
[This retracts the article DOI: 10.1007/s11277-021-08436-w.].
-Extremely-Low Frequencies (ELF, 30∼300Hz) transmitting antennas in wireless communications are often limited by antenna size and complex impedance matching networks. In this paper, we propose an ultra-small Artificial Electret Type Mechanical Antenna (AETMA), which is composed of a single charge electret and a driving structure, with high radiation efficiency and small size. In order to improve the electric dipole moment of the mechanical antenna, we employ a pin-plate corona polarization technique and a unidirectional stretching treatment to obtain a porous thin-film electret that can stably store a large amount of charge. Its surface charge density can reach 5.355 mC/m2 and we analyze its surface potential stability. To assess the radiation capability of AETMA, the radiation field models of three kinds of mechanical antennas are established and verified by simulation. Additionally, we simulate and compare the planar electret and curved electret configurations to determine the optimal form of AETMA. The radiation intensity of the planar electret is found to be superior under the same moment of inertia. Finally, a 1m-scale artificial electret antenna is designed based on the optimal model. Comparative analysis with existing rotary mechanical antenna schemes confirms the great potential of the proposed AETMA for portable, miniaturized and high-performance wireless communication devices.
This paper introduces the enhancement of Visible Light Communications (VLC) for V2V using artificial intelligence models. Different V2V scenarios are simulated. The first scenario considers a specific longitudinal separation and a variable lateral shift between vehicles. The second scenario assumes random longitudinal separation and a specific lateral shift between vehicles. Significant obstacles that impair performance and dependability in V2V communication systems include bit errors, high power consumption, and interference. By combining Convolutional Neural Networks (CNNs), Generative Adversarial Network (GAN), Gated Recurrent Unit (GRU), and Deep Denoising Autoencoder (DDAE), this paper suggests a deep learning-based system to address these issues. The framework comprises four modules, a power reduction module that uses a GAN to generate low-power signals while maintaining signal quality; a performance enhancement module that uses GRU, a Bit Error Rate (BER) reduction module that uses a DDAE to denoise the received signal and minimize errors; and an interference cancellation module that uses a CNN-based U-Net to separate the desired signal from interference. It is shown that the suggested model significantly improves throughput, power efficiency, BER reduction, and interference cancellation. In dynamic and noisy contexts, our study offers a reliable and scalable way to improve the performance and dependability of V2V communication systems. The CNN-U-Net-GAN-GRU-DDAE model outperforms other models, including CNN-U-Net, CNN-U-Net-GAN, and CNN-U-Net-GAN-GRU, achieving the best results by an average percentage 13.6%, 14.4% and 4.2% respectively. By comparing this work with previous works, we deduce that the improving average percentage for our work by 31.7%.
In trainable wireless communications systems, the use of deep learning for over-the-air training aims to address the discontinuity in backpropagation learning caused by the channel environment. The primary methods supporting this learning procedure either directly approximate the backpropagation gradients using techniques derived from reinforcement learning, or explicitly model the channel environment by training a generative channel model. In both cases, over-the-air training of transmitter and receiver requires a feedback channel to sound the channel environment and obtain measurements of the learning objective. The use of continuous feedback not only demands extra system resources but also makes the training process more susceptible to adversarial attacks. Conversely, opting for a feedback-free approach to train the models over the forward link, exclusively on the receiver side, could pose challenges to reliably end the training process without intermittent testing over the actual channel environment. In this article, we propose a novel method for the over-the-air training of wireless communication systems that does not require a feedback channel to train the transmitter and receiver. Random samples are transmitted through the channel environment to train a mixture density network to approximate the channel distribution on the receiver side of the network. The transmitter and receiver models are trained with the resulting channel model, and the transmitter can be deployed after training. We show that the block error rate measurements obtained with the simulated channel are suitable for monitoring as a stopping criterion during the training process. The resulting method is demonstrated to have equivalent performance to the end-to-end autoencoder training on small message sequences.
The rapid development of high-speed railways, coupled with the swift advancement of related wireless services, has raised public concerns about electromagnetic exposure, particularly for residents along railway lines. While numerous studies have examined radio exposure from mobile operators, broadcasting, and WLAN services, the electromagnetic exposure associated with railway communication services has primarily focused on occupational exposure for relevant personnel, with insufficient attention given to public exposure near railway lines. In this study, electromagnetic exposure levels along two railways in Lanzhou, China were assessed at two different dates using both vehicle-mounted data measurement and fixed-location measurement methods.It was observed that the maximum electromagnetic exposure caused by GSM-R occurs at distances of 1200 to 1500 m from the base station. The maximum values, 95 % values and mean values of the electromagnetic exposure for GSM-R were recorded respectively: 0.5755 V/m, 0.2265 V/m and 0.02483 V/m (Lanzhou-Xinjiang railway);0.1376 V/m, 0.1107 V/m and 0.01722 V/m(Lanzhou-Lanzhou New District railway). The data collected at fixed locations during the same time period were 0.0313 V/m, 0.0303 V/m and 0.02517 V/m, respectively. The measurements also exhibited significant spatial variability, yet those taken on different dates showed high reproducibility. Additionally, a phenomenon of channel switching of GSM-R service was noted during the measurements. The vehicle-mounted data measurement method is highly efficient for assessing electromagnetic exposure levels over large areas. Integrating additional data, such as GIS(Geographic Information System) and base station information, allows for multi-dimensional analysis, uncovering more exposure-related insights. Our study, utilizing this approach, found that the variability of GSM-R exposure along the railway may be related to the vertical directionality of the antennas. Furthermore, the electromagnetic exposure levels from the GSM-R service were found to comply with ICNIRP guidelines, indicating that these communication services present no significant health risks to the surrounding public.
In the everyday world of computer applications, from the cloud to the Internet of Things, distributed sensor networks are essential (IoT). These computer application devices are often connected to Arduino network connection and microcontrollers such sensors and actuators. Thus, a defensive network with an IDS serves as the need for contemporary networks. The intrusion detection system has unavoidably evolved throughout the years, but despite this, it remains a difficult study topic since the current intrusion detection system uses signature-based approaches rather than anomaly detection. Therefore, improving the current intrusion detection system is challenging since it is difficult to find zero-day attacks in IoT networks when dealing with varied data sources. Filtered Deep Learning Model for Intrusion Detection with a Data Communication Approach is presented in this study. The five steps that make up the suggested model are Initialization of Sensor Networks, Cluster Formation and Head Selection, Connectivity, Attack Detection, and Data Broker. It was discovered that the suggested model for intrusion detection outperformed both the current Deep Learning Neural Net and Artificial Neural Network. In comparison to the most popular algorithms, experimental findings revealed a superior result of 96.12 % accuracy. The E-shaped patch antenna is a brand-new single-patch wide-band microstrip antenna that is presented in this research. A microstrip antenna's patch has two parallel slots built into it to increase its bandwidth. Investigating the behaviour of the currents on the patch allows for the exploration of the wide-band mechanism. A broad bandwidth is achieved by optimising the slot's length, breadth, and location. Finally, a 40.3 % E-shaped patch antenna is developed, made, and tested to resonate at 7.5 and 8.5 GHz for wireless communications. Additionally displayed are the reflection coefficient, VSWR, radiation pattern and directivity.
The rapid development of the Internet of Things (IoT) has extensively promoted the development of Wireless Sensor Networks (WSNs), an essential technology for series displaying perception and data collected from the physical world. In densely distributed areas, sensor nodes are unevenly distributed, which leads to the network coverage build-up and the consequent efficiency and effectiveness of WSNs. To address this issue, this paper proposes a new method for WSN coverage optimization based on the Reptile Search Algorithm (RSA). In the past, the Reptile Search algorithm has been used to solve optimization problems, which means it can improve different processes. However, the RSA needs to track the trajectory of optimal individuals in each iteration, which will ignore non-optimal individuals' bioeconomic characteristics. Therefore, the paper introduces a distribution estimation strategy into the RSA framework, which can fully mine all the positional information hidden in the entire population. We selected several functions as optimization test benchmark functions to evaluate the feasibility of the proposed method. This paper compares the proposed improved RSA with the standard RSA and some traditional optimization algorithms. The result has been calculated through a series of experiments on network coverage optimization, and the change of parameters also determines the effect of the RSA in the optimization of network coverage. The simulated results of the three similar network coverage optimization experiments show that the improved RSA can be used efficiently within different scenarios.
A compact high bandwidth ratio (BDR) super wide band flower slotted micro strip patch antenna (SWB-FSMPA) for super wide band (SWB) applications is presented. The SWB-FSMPA is constructed on a FR-4 substrate having a size of 16 × 22 mm2. The SWB-FSMPA incorporates a 50 Ω tapered micro strip line and a rectangular beveled defected ground structure (RB-DGS). This design enables a simulation bandwidth from 3.78 to 109.86 GHz, allowing for coverage of various wireless applications such as WiMAX (3.3-3.6 GHz), 5G (3.3-3.7 GHz), WLAN (5.15-5.825 GHz), UWB (3.1-10.6 GHz), Ku- (12-18 GHz), K- (18-27 GHz), Ka- (27-40 GHz), V- (40-75 GHz), and W- (75-110 GHz) millimeter wave bands. The SWB-FSMPA antenna exhibits a gain that varies within the range of 3.22-7.23 dBi and a peak efficiency of 93.3 %. The SWB-FSMPA possesses a bandwidth ratio (BR) of 29.1:1, a BDR of 5284 in the frequency domain, a minimal group delay (GD) fluctuation of <0.48 ns, and a linear phase in the time domain, making it well-suited for SWB applications.
Comprehensive and exceedingly precise centralized patient monitoring has become essential to advance predictive, preventive, and efficient patient care in contemporary healthcare. Millimeter-wave (mmWave) technology, boasting high-frequency and high-speed wireless communication, holds promise as a viable solution to this challenge. This paper presents a new approach that combines mmWave communication and computer vision (CV) to achieve real-time patient monitoring and data transmission in indoor medical environments. The system comprises a transmitter, a reflective surface, and multiple communication targets, and utilizes the high-frequency, low-latency features of mmWave as well as CV-based target detection and depth estimation for precise localization and reliable data transmission. A machine learning algorithm analyses real-time images captured by an optical camera to identify target distance and direction and establish clear line-of-sight links. The system proactively adapts its transmission power and channel allocation based on the target's movements, guaranteeing complete coverage, even in potentially obstructive areas. This methodology tackles the escalating demand for high-speed, real-time data processing in modern healthcare, significantly enhancing its delivery.
This paper presents a conformal, miniaturized, and geometrically simple monopole antenna designed for Vehicle-to-Everything (V2X) communications. The antenna consists of a flexible substrate, radiating patch, ground, and metallic stubs. Meandered lines are added to the U-shaped radiator to achieve the required bandwidth of the antenna. The antenna has |S11|< -10 dB magnitude from 5.06 to 7.24 GHz, attaining a peak low magnitude of-68 dB. The antenna is configured into a 4-port Multiple-Input-Multiple-Output (MIMO) setup to minimize the mutual coupling between its elements. The proposed flexible MIMO antenna offers bandwidth from 5.37 to 7.34 GHz and a peak moderate gain of 4.63 dBi with omnidirectional stable radiation patterns. To improve the mutual coupling, two hollow concentric circular structures, in combination with a pair of stub networks are integrated between the elements of the MIMO system. The transmission coefficient and surface current analysis confirm the effectiveness of the decoupling structure. The presented MIMO antenna is characterized by high isolation, a low envelope correlation coefficient (ECC), and high diversity gain, suitable for V2X MIMO communication scenarios.
This study describes a high-gain, ultra-wideband quad-port THz MIMO antenna designed for 6G, TWPAN, and next-generation wireless communications. The design uses fractal radiating elements, graphene-based tunability, and a defective ground structure (DGS) to improve bandwidth, isolation, and impedance matching. The antenna's tiny polyimide substrate (130 × 130 μm²) enables an ultra-wide bandwidth of 62 THz, a peak gain of 15.24 dB, and port-to-port isolation of 43 dB, resulting in strong MIMO performance. Integrating metasurface structures and graphene tuning allows for dynamic frequency reconfiguration, making it suitable for a wide range of wireless applications. The performance study reveals good spatial diversity and low signal distortion, with an envelope correlation coefficient (ECC) of less than 0.05 and a Diversity Gain (DG) of nearly 10 dB. Additionally, the Total Active Reflection Coefficient (TARC) and Channel Capacity Loss (CCL) are kept to a minimum, maximising spectral efficiency. UV photolithography and electron beam evaporation (EBE) are employed to fabricate the antenna, yielding high precision and minimal losses. Compared to existing designs, it outperforms them in terms of gain, isolation, and multi-band operation. The suggested THz MIMO antenna's scalability, compact form factor, and customisable properties make it an attractive choice for future 6G wireless networks, sub-THz IoT systems, and ultra-fast personal area networks (PANs). Future research will focus on adaptive beamforming, real-world prototypes, and experimental validation to improve its application in next-generation THz communication systems.
This paper presents a dual-purpose LED driver system that functions as both a lighting source and a Visible Light Communication (VLC) transmitter integrated with a Powerline Communication (PLC) network under the PRIME G3 standard. The system decodes PLC messages from the powerline grid and transmits the information via LED light to an optical receiver under a binary phase shift keying (BPSK) modulation. The load design targets a light flux of 800 lumens, suitable for LED light bulb applications up to 10 watts, ensuring practicality and energy efficiency. The Universal Asynchronous Receiver-Transmitter (UART) module enables communication between the PLC and VLC systems, allowing for an LED driver with dynamic control and real-time operation. Key signal processing stages are commented and developed, including a hybrid buck converter with modulation capabilities and a nonlinear optical receiver to regenerate the BPSK reference signal for VLC. Results show a successful prototype working under a laboratory environment. Experimental validation shows successful transmission of bit streams from the PLC grid to the VLC setup. A design guideline is presented in order to dictate the design of the electronic devices involved in the experiment. Finally, this research highlights the feasibility of integrating PLC and VLC technologies, offering an efficient and cost-effective solution for data transmission over existing infrastructure.
Ultra-wideband (UWB) technology offers a unique alternative for communicating over short distances due to its high capacity and low power consumption during transmission and reception. However, designing an antenna with appropriate characteristics for these systems is a significant problem since it must avoid interfering with current limited bandwidth technologies (e.g., Wi-Fi, lower 5G, and satellite downlink band) in the UWB range. In this review article, we have made a comprehensive literature review of quasi-perfect-notch band UWB antennas that have been reported with different techniques used. The ever-growing demand for antennas in wireless communications has challenged researchers to design antennas with innovations for highly efficient communications between end users. Adding external filters has the disadvantage of large volume and high cost. To overcome these problems the researchers have proposed different band-notch techniques to mitigate the interferences in this densely populated wireless network era. The primary objective of this review article is to offer comprehensive insights into various highly selective notch-band UWB antennas, presenting a clearer overview of the techniques used to mitigate interference from narrowband signals that overlap with the UWB spectrum. The methods used-which include applying EBG structures, incorporating SRRs, slot etching, and using stubs-are covered in detail in different sections.
This work aims to provide an effective hybrid beam forming method with Dual-Deep-Network to overcome overhead for mm-wave massive MIMO systems. In this paper, a Dual-Deep-Network technique is described for the extraction of statistical structures from a hybrid beam forming model based on mmWave logics, as well as training logic for the network map functions. The proposed approach of DDN is trained with proper data sequences used for communication and the training phase is conducted with the norms of numerous channel variants. With the nature of diverse channel states, a Dual-Deep-Network is required to manipulate the level of presence and abilities even after training as well. The performance level improvements are practically summarized in both the transmission and reception entities with the help of the proposed hybrid network architecture and the associated Dual Deep Network algorithm. Specifically, the BER versus SNR and spectral efficiency versus SNR are evaluated as well as the resulting accuracy levels are cross validated with numerous classical communication techniques. This paper shows the processing difficulties of the proposed approach and typically cross-validates with other beam forming logics. The computational cost and performance estimations are improved, and the metrics are clearly visualized on this paper based on improved beamforming procedures as well as the proposed approach of DDN based Multi-Resolution Code Book performance metrics are estimated clearly with proper mathematical model investigations. With 7Kbits/s/Hz and 1e-1, respectively, the key metrics of spectral efficiency and BER are enhanced.