Modern Communication Systems Research Articles

A high‐power coaxial to rectangular waveguide transition

AbstractThe high‐power characteristic of the coaxial to waveguide transition plays an important role in modern communication and radar systems. In this letter, a novel design of a high‐power transition from a 50 Ω coaxial line with an N‐type connector to a WR‐90 rectangular waveguide. The power handling capability (PHC) of the proposed structure is significantly enhanced by a cone‐shaped excitation probe with a bold end, a transitional cavity, and a mode conversion cavity with a curved shorted back wall. The simulated results reveal that the transition possesses high PHC of 257 kW at 8.5 GHz, which is the upper limit of the power tolerance for the N‐type connector. The measured results show that the return loss and insertion loss are better than 15 and 0.15 dB, respectively, in the range of 7.96–9.16 GHz. In addition, experimental validation confirms the normal operation of the structure under a 250 kW power level test, demonstrating the applicability of the proposed transition in high‐power microwave systems.

A Statistical Analysis of the Morphology of Storm‐Enhanced Density Plumes Over the North American Sector

AbstractThe storm‐enhanced density (SED) is a large‐scale midlatitude ionospheric electron density enhancement in the local afternoon sector, which exhibits substantial spatial gradients and thus can impose detrimental effects on modern navigation and communication systems, causing potential space weather hazards. This study has identified a comprehensive list of 49 SED events over the continental US and adjacent regions, by examining strong geomagnetic storms occurring between 2000 and 2023. The ground‐based Global Navigation Satellite System (GNSS) total electron content and data from a new TEC‐based ionospheric data assimilation system were used to analyze the characteristics of SED. For each derived SED events, we have quantified its morphology by employing a Gaussian function to parameterize key characteristics of the SED, such as the plume intensity, central longitude, and half‐width. A statistical analysis of SEDs was conducted for the first time to characterize their climatological features. We found that the SED distribution exhibits a higher peak intensity and a narrower width as geomagnetic activity strengthens. The peak intensity of SED has maximum values around the equinoxes in their seasonal distribution. Additionally, we observed a solar cycle dependence in the SED distribution, with more events occurring during the solar maximum and declining phases compared to the solar minimum. SED plumes exhibit a sub‐corotation feature with respect to the Earth, characterized by a westward drift speed between 50 and 400 m/s and a duration of 3–10 hr. These information advanced the current understanding of the spatial‐temporal variation of SED characteristics.

Analysis of Machine Learning Methods for Intrusion Detection Systems in Wireless Networks

Wireless networks have become integral to modern communication systems, making them vulnerable to various security threats. Intrusion detection systems (IDSs) are essential for detecting and mitigating these threats and can also powerfully screen network traffic for pernicious activities that are planned to abuse the classification, honesty, realness, and accessibility of the network. Machine learning (ML) and Deep learning (DL) techniques are effective in identifying and classifying network attacks. This study proposes a novel intrusion detection system that employs ML and DL models to classify and distinguish network attacks in wireless networks. The proposed system enhanced detection accuracy and efficiency in IDS, the scalability of IDS systems, and estimated the performance of IDS in wireless networks. It also investigates IDS techniques using machine learning, designs and implements IDS in wireless networks using machine learning, and trains several IDS models regarding wireless networks that are fitted. It contrasts the exhibition of proposed models and existing procedures. The suggested system can therefore be utilized as an effective IDS for wireless networks, providing real-time detection and classification of network attacks.

Exploration and Research on Mobile Device Communication Protocols Based on Heterogeneous Networks

With the widespread adoption of mobile devices and the rapid development of network technology, heterogeneous networks have become a vital component of modern communication systems. This paper aims to explore and research communication protocols for mobile devices based on heterogeneous networks to enhance communication efficiency and user experience. Initially, the paper outlines the composition, characteristics, and key technologies of heterogeneous networks, analyzing the classification, advantages, disadvantages, and development trends of existing mobile device communication protocols. On this basis, a new communication protocol design scheme is proposed, considering the characteristics of heterogeneous networks, aiming to achieve efficient and reliable communication. Through the construction of an evaluation index system and the setup of a simulation environment, comprehensive tests and analyses of the new protocol's performance are conducted. Case studies further validate the effectiveness of the protocol in real-world applications. The research results indicate that the proposed communication protocol can significantly enhance the communication performance of mobile devices in heterogeneous network environments. The findings of this study hold significant theoretical and practical implications for advancing communication technology in heterogeneous networks.

Revolutionizing connectivity: Unleashing the power of 5G wireless networks enhanced by artificial intelligence for a smarter future

In the context of 5G wireless networks, this paper addresses a crucial concern—data loss in Analog-to-Digital Converters (ADCs) embedded within Multiple Input Multiple Output (MIMO) channels, a prevalent challenge in modern wireless communication systems. Despite the integration of ADCs in MIMO configurations to mitigate this issue, their implementation introduces significant complexities. In response to this, the paper introduces an innovative and efficient approach aimed at enhancing channel estimation in 5G MIMO systems. The proposed methodology relies on a heuristic-based optimization technique within a channel prediction framework. The framework uses feedback information collected through a Cascade Fusion Serial Network (CFSN) to analyze the receiver-side error ratio and estimate the channel coefficients of MIMO systems at the transmitter. An attention mechanism is incorporated into the model design through the use of a Convolutional Neural Networks - Gated Recurrent Units (CNN-GRU) and a Multi-Scaled Stacked Auto encoder. Parameters of Attention CNN-GRU, Stacked Auto encoder, and CFSN are refined using the Whale Optimization method. The optimization objective is to minimize key metrics, including Root Mean Squared Error (RMSE), Bit Error Rate (BER), and Mean Squared Error (MSE) associated with the estimated channel. Experimental evaluations underscore the efficacy of the proposed MIMO model in the context of 5G networks. The results demonstrate improved convergence rates, heightened prediction performance, and reduced computational costs compared to existing methods. This research contributes valuable insights into addressing data loss challenges specific to 5G MIMO systems, providing a pioneering solution that integrates heuristic optimization, advanced neural network architectures, and nature-inspired algorithms for parameter tuning.

Evaluation of Joint Technique Iterative Clipping Filtering (ICF) and Neural Network Predistortion on SDR-based MIMO-OFDM System

Multiple-input, multiple-output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is a communications technology that powers numerous modern communication systems, including 5G and WiFi-6. This technology is utilized in current communication systems due to its high performance and extensive channel capacity. MIMO-OFDM does have disadvantages, such as large Peak-to-Average Power Ratio (PAPR) values. If the signal is processed by a nonlinear Power Amplifier (PA) device, a high PAPR value signal can result in both in-band and out-of-band signal distortion. To combat high PAPR values, PAPR reduction strategies such as Iterative Clipping Filtering (ICF) are utilized. From this study, using ICF with iteration 2 and Clipping Ratios (CR) 3 and 4 can improve the system's minimum Bit Error Rate (BER) by about 22.8% and 91.1%, respectively. Choosing the correct CR will improve the system, but using the lower CR will make it worse than a system without ICF. This occurs in systems using ICF with iterations two and CR 2 and at the same SNR conditions as systems without ICF; using ICF with iterations two and CR 2 results in higher BER values. The use of Predistortion Neural Network (PDNN) can overcome this problem. By using PDNN, there is an improvement in the system where the minimum BER value can reach 0.1 × 10-5. The percentage decrease in BER from using PDNN for ICF with iterations two and CR 2, 3, and 4 is 99.88%, 99.86%, and 98.807%, respectively. Thus, the joint techniques of ICF and PDNN can significantly enhance the performance of MIMO-OFDM systems with nonlinear PA. Importantly, the experiment was conducted on an SDR device, ensuring the real-world applicability of the results.

DeepRoughNetID: A Robust Framework for Network Anomaly Intrusion Detection with High Detection Rates

Network security faces challenges, including reduced true positives, increased false positives, and inadequate anomaly detection efficacy. This paper introduces the DeepRoughNetID (DRNID) approach to address these issues and confront the increasing cyberattack threat in modern communication systems. DRNID presents an innovative framework for network intrusion detection, incorporating kNNImputer, GreedyRoughSelector, DeepVAEEnsembler, and IntrusionNet components. kNNImputer enables adaptable data preprocessing by leveraging instance-based learning, facilitating efficient handling of evolving datasets without necessitating full retraining. GreedyRoughSelector enhances classifier performance through systematic attribute selection, focusing on relevant features while eliminating redundancies. DeepVAEEnsembler leverages Variational Autoencoders (VAEs) to learn underlying data distributions, enabling robust anomaly detection. IntrusionNet utilizes VAEs to classify intrusions, providing a comprehensive solution to network security challenges. By integrating these components, DRNID offers a refined approach that promises significant advancements in network intrusion detection. The experimental results demonstrate the effectiveness of DRNID, with an impressive accuracy of 98.8% achieved through 10-fold cross-validation. DRNID exhibits superior performance compared to existing methods across various datasets, showcasing its robust anomaly detection capabilities. This methodology empowers organizations to proactively prevent security violations and safeguard sensitive data from malicious entities. Beyond its theoretical contributions, our research carries tangible implications for strengthening cybersecurity defenses across diverse sectors, including telecommunications, finance, and healthcare.

Simulation of Bismuth on-doped Fibre Lasers in the 1250-1300 nm Band

Optical fiber transmission has become the predominant mode of transmission in modern communication systems. The extensive bandwidth potential of optical fiber makes it crucial for achieving high-capacity transmission. With the successful development of anhydrous peak fiber, the issue of harmonic absorption caused by OH molecules has been effectively resolved. This novel fiber establishes a low-loss window within the wavelength range from 1280 nm to 1625 nm, thereby providing enhanced transmission capacity for fiber optic communication systems. By increasing WDM density, which involves the simultaneous utilization of multiple light signals with different wavelengths in the fiber, higher transmission capacity can be achieved. This paper presents a comprehensive simulation study on bismuth-doped fiber lasers operating within the 1250-1300nm band. Specifically, this study investigates the numerical relationship between laser power, threshold power, and pump power. The simulation results demonstrate that a threshold power level of approximately 1.5W is required for laser generation; no laser emission occurs below this threshold. Once the pumping power surpasses this threshold value, laser operation commences and output power increases proportionally with further increments in pumping power.

Exploring and Analyzing a Hybrid PAPR Reduction Method for OFDM Optimization

Orthogonal Frequency Division Multiplexing (OFDM) is a effective modulation technique widely used in modern communication systems, including 5G and wireless networks because of its high spectral efficiency and robustness towards multipath fading. However, OFDM signals are characterized by a high Peak-to-Average Power Ratio (PAPR), which can severely impact the power efficiency and operational cost of these systems. This research investigates and analyse effectiveness of two conventional PAPR reduction methods that is Selective Mapping (SLM), and Partial Transmit Sequence (PTS) with incorporating their approach of PAPR reduction to develop a hybrid variant: SLM-PTS. This study utilized MATLAB simulations to implement and test each PAPR reduction technique on a standard OFDM system model. Parameters such as the number of subcarriers, oversampling factor, and modulation types were systematically varied to analyze the impact on PAPR, BER, and computational complexity. The effectiveness of each technique was measured using the Complementary Cumulative Distribution Function (CCDF) of PAPR values and BER performance under different channel conditions. Key findings reveal that hybrid techniques, particularly SLM & PTS, offer significant improvements in PAPR reduction compared to traditional methods alone, without a corresponding increase in BER or computational overhead. The research underscores the potential of hybrid PAPR reduction techniques to significantly enhance the efficiency and reliability of OFDM systems. These findings have important implications for the design and optimization of next-generation wireless communication systems, suggesting that hybrid approaches can provide a balanced solution to the longstanding issue of PAPR in OFDM transmissions. This study contributes valuable insights into the practical application of combined PAPR reduction strategies, offering a pathway toward more power-efficient and robust OFDM technologies. Keywords : OFDM, PAPR, CCDF, SLM and PTS

Compact active analog device for novel applications useful for sensing and measurement

The presented research introduces a novel approach to modern modular active device and provides various practical application examples tailored for industrial sensing readouts. Straightforward implementation of single active device-based topologies in frequency bands from kHz up to MHz is presented. These applications include: an electronically linearly tunable special band-pass filter that can be easily modified into a voltage-controlled oscillator, a two-port quadratic transformer, an amplitude modulator, and an equalization circuit (which serves as a fractional-order differentiator and integrator). These circuits benefit from high-impedance voltage input and low-impedance output (easily matched to 50 Ω), high processed signal levels (often reaching hundreds of mV), and simplified topologies without redundant passive elements. These features are especially valuable in tunable and configurable solutions for modern communication, sensing, audio, and consumer electronic systems, as well as in modeling various physical and biological systems. The experiments have been provided by simulations and measurements using device VCA824.

ДОСЛІДЖЕННЯ КОНТЕКСТНО-ЧУТЛИВОГО АЛГОРИТМУ МОНІТОРИНГУ КІБЕРБЕЗПЕКИ НА ОСНОВІ РЕКУРЕНТНИХ НЕЙРОННИХ МЕРЕЖ

The most common problems faced by modern information and communication systems (ICS) in the context of combating cyber threats were examined in the paper. The importance of ensuring the reliable operation of ICS, and protecting their users' private data from unauthorized interception or destruction was emphasized. The main principles of effective protection of ICS systems against possible interference in their work were defined. The classification of cyber threats and their impact on the functioning of information systems was presented. Features of the use of modern information technologies were determined, such as machine learning (ML), and recurrent neural networks (RNN) for increasing the effectiveness of detecting and preventing such threats, speeding up the process of calculating large volumes of information about various aspects of the work of information and communication systems. The parameters of the analysis of ICS behavior, which indicate the presence of problems in cyber security, were studied. The features and advantages of deploying RNN in ICS were analyzed, which makes it possible to simplify the tasks of cyber defense. A modified context-sensitive algorithm for cyber security monitoring (CCM-RNN) was proposed, which is based on RNN and allows taking into account the dynamics of system changes in the established context, for example, the type or volume of traffic from users, etc. The method of selecting the most effective parameters and properties of ICS for detecting cyber threats was improved. The results of the study of the effectiveness of the use of the modified CCM-RNN algorithm demonstrated its broad capabilities for fast and accurate detection of anomalies in the operation of ICs that may threaten their cyber security. By changing the number of properties of the CCM-RNN algorithm, which correspond to the characteristics of various aspects of the IC, it is possible to achieve the maximum accuracy of cyber threat detection. The modified algorithm also allows for the reduction of the duration of calculations during analysis. Based on the research results, a conclusion was made about the feasibility of using the proposed modified CCM-RNN algorithm for the ability to detect cyber security threats in ICS by flexibly adjusting the number and type of learning parameters of neural networks. In this way, the accuracy and duration of calculations were optimized, as well as the peculiarities and contexts of information and communication systems were taken into account.

Exploring Fault Tolerance Consensus for Wireless Sensor Networks: A Comprehensive Detailed Study

The integration of wireless networks into modern communication systems has revolutionized information exchange across various applications. However, achieving reliable agreement in these networks is significantly impeded by their unique properties, such as blurring, interruption, and transparency. A fundamental component of distributed systems, fault-tolerant consensus ensures that nodes in the network can agree on a consistent value even in the presence of malfunctioning or corrupted elements. This study explores both non-Byzantine and Byzantine fault-tolerant consensus approaches, with a focus on achieving agreement in the presence of benign faults. The importance of fault-tolerant consensus in wireless connections is underscored, particularly in applications like wireless blockchain, IoT, and vehicular networks. The research delves into wireless network-specific fault-tolerant consensus algorithms to address the specific challenges posed by networking environments. Furthermore, a comparative analysis of Byzantine Fault Tolerance mechanisms in distributed systems is provided to shed light on their features and benefits. It highlights the importance of ensuring system reliability and consistency in wireless networks to maintain seamless communication and data integrity. Moreover, the paper emphasizes the critical role of fault-tolerant consensus in enhancing system resilience and performance. It underscores the need for robust algorithms and protocols to detect and mitigate errors, ensuring reliable communication and coordination despite potential node failures. By providing insights into fault-tolerant consensus mechanisms tailored to dynamic conditions, the study aims to optimize system adaptation and effectively mitigate both Byzantine and non-Byzantine failures in wireless network environments.

Microwave Antenna Optimization for Low Latency and High Throughput Communication Systems

This paper presents a comprehensive exploration of microwave antenna optimization strategies aimed at achieving low latency and high throughput in modern communication systems. With the escalating demand for real-time applications and data-intensive services, the optimization of microwave antennas has become imperative to meet the stringent requirements of responsiveness and efficiency. The abstract delves into various facets of antenna optimization, including design parameters, signal processing techniques, and deployment considerations, all geared towards minimizing latency and maximizing throughput. Through a thorough review of existing literature and practical insights from real-world deployments, this research aims to provide valuable guidance for engineers and researchers in designing and implementing optimized microwave antennas for next-generation communication networks. By examining the intricate interplay between antenna design, signal processing algorithms, and deployment strategies, this paper elucidates the challenges and opportunities in achieving low latency and high throughput in microwave communication systems. Furthermore, it highlights the significance of factors such as gain, beamwidth, polarization, frequency band selection, multiple-input multiple-output (MIMO) systems, beamforming, adaptive coding and modulation (ACM), antenna placement, tower height, and line-of-sight considerations in optimizing antenna performance. The findings presented in this paper contribute to advancing the understanding of microwave antenna optimization and offer practical insights into enhancing the responsiveness, efficiency, and reliability of modern communication networks.

Enhancing PAPR reduction efficiency in MIMO-OFDM systems via selective mapping and metaheuristic algorithms

The relentless evolution of communication systems, driven by the demands of 5G and the impending 6G networks, necessitates heightened data rates and spectral efficiency. orthogonal frequency division multiplexing (OFDM), a form of multicarrier modulation employed in multi-input multi-output (MIMO) systems, stands as a pivotal technology. Yet, OFDM grapples with challenges, notably the peak-to-average power ratio (PAPR) issue. Selective mapping (SLM) has been a favored technique for mitigating PAPR in OFDM, albeit challenged by computational complexities in its pursuit of discovering optimal phase factors. This paper pioneers a transformative approach by integrating metaheuristic algorithms genetic algorithm (GA), particle swarm optimization (PSO), and the innovative fireworks algorithm (FWA) into SLM for PAPR reduction while minimizing computational complexity. Simulation results not only affirm the efficacy of SLM-based techniques but also spotlight the potential of metaheuristic algorithms in addressing PAPR challenges in modern communication systems. The study transcends single-antenna systems, extending to MIMO-OFDM systems based on WiMAX standards, validating the efficacy of these techniques in multi-antenna configurations. Crucially, the FWA, proposed for the first time in this paper, emerges as a robust candidate, striking an enviable balance between computational efficiency and performance, achieving a notable PAPR reduction with a favorable search number.

ORBITAL ANGULAR MOMENTUM-BASED SLOT ARRAY ANTENNA FOR MODERN APPLICATIONS

Millimeter waves are the most promising waves for different modern wireless communication networks. This is due to their ability to carry big data with enhanced channel capacity. However, mm-waves suffer from low propagation due to the effects of high attenuation factors with high frequencies. Therefore, in this work, a three-dimensional structural antenna design with a slot array is proposed to achieve high gain through a two-port feeding operation for orbital angular momentum applications of modern communication systems. Also, the improved bandwidth of the proposed antenna makes it an excellent candidate for fifth-generation (5G) communications systems. The antenna depends on two concentric structures with conductive circular cross-sectional area. The first structure is shaped as a three-dimensional cone with a cylindrical waveguide. The second structure is shaped as a circular disc with a circular aperture. The two parts of the proposed antenna are concentered to be fed with two discrete ports from the side with a 900phase difference. It is determined that the proposed antenna offers an exceptional gain to vary from 10dBi to 20dBi within the frequency band from 3GHz to 30GHz. It is realized that such enhancement after introducing a conductive circular reflector underneath the conductive cone. The antenna shows a matching impedance below -10dB over the entire frequency band of interest. The antenna radiation proprietors at 25GHz and 26GHz at the E-plane and H-plane are characterized. Finally, the antenna performance is validated using two software packages based on CST studio and HFSS algorithms.

Supercontinuum Generation by Controlling Pitch in Photonic Crystal Fibers

The influence of varying the distance between air holes (pitch) on the geography of solitone propagation through a photonic crystal fiber has been tested. The study depends on the Split-Step Fourier method and the results quantified using MATLAB. The first-order solitone was tested with the change in pitch, and it was found that there is a clear decay in the amplitude of the resulting pulse with an increase in pitch. When increasing the pitch in the case of second-order solitons, it was noticed that the pulse would split into multiple-order solitons down to higher-order solitons with the increase in pitch. In the case of third-order solitons, solitonic fission leads to the supercontinuum generation with increasing the pitch, where the supercontinuum generation was reached in this way depending on a very small energy source compared to the high energies used to generate this type of spectrum by the previous methods. In this study, I observed that when the pitch values increased in the third-order soliton, this result led to the use of supercontinuum generation (ScG) which has many applications such as medical and industrial applications and has an important role in modern communication systems. Keywords: Photonic Crystal Fiber, Split-Step Fourier, Soliton Pulse, Pitch, Supercontinuum Generation.

Communication spectrum prediction method based on convolutional gated recurrent unit network

In modern wireless communication systems, the scarcity of spectrum resources poses challenges to the performance and efficiency of the system. Spectrum prediction technology can help systems better plan and schedule resources to respond to the dynamic changes in spectrum. Dynamic change in the spectrum refers to the changes in the radio spectrum in a wireless communication system. It means that the available spectrum resources may change at different times and locations. In response to this current situation, this study first constructs a communication collaborative spectrum sensing model using channel aliasing dense connection networks. Then, combining convolutional neural network and gated cyclic unit network in deep learning technology, a communication spectrum prediction model is built. It aims to achieve accurate perception and prediction of spectrum resources through the aforementioned spectrum sensing and prediction models. The results confirm that the proposed perception model has inconsistent perception accuracy under different number of secondary users, with a maximum of 0.99. It is verified that the proposed spectrum prediction model achieves a high prediction accuracy of 0.95 within 208 s and its performance outperforms current similar models. The results are based on the model's deep learning analysis of massive historical communication data, in which the optimized shuffle dense net model plus convolutional gated recurrent unit model is the key to achieve fast and accurate prediction. On the contrary, the highest spectrum prediction accuracy of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks-Long Short-Term Memory (ConvLSTM) models are 0.86, 0.90, and 0.85, respectively. And the model needs to run for a longer period of time, up to 324, for ConvLSTM to reach the prediction accuracy value of 0.95. In summary, the perception and prediction model built by this research has good performance, and its application in the field of wireless communication can assist staff in better monitoring spectral changes, thereby making more efficient use of spectral resources.

Dual-band MIMO antenna with low mutual coupling for 2.4/5.8 GHz communication and wearable technologies.

To satisfy the requirements of modern communication systems and wearables using 2.4/5.8 GHz band this paper presents a simple, compact, and dual-band solution. The antenna is extracted from a circular monopole by inserting various patches and stubs. The genetic algorithm is utilized to optimize the parameters and achieve the best possible results regarding bandwidth and gain. Afterward, a 2-port multiple-input-multiple-output (MIMO) configuration is created by positioning an identical second single element perpendicularly to the first one. The electrical size of the suggested MIMO configuration is 0.26 λL × 0.53 λL, where λL represents the free space wavelength at lower resonance of 2.45 GHz. The common ground technique is adopted to further reduce and achieve the accepted level of mutual coupling of the MIMO configuration. The presented MIMO antenna offers a low mutual coupling of < -27 dB with 0.2 envelope correlation coefficient (ECC). The antenna has a gain of around 6.2 dBi and 6.5 dBi at resonating frequencies of 2.45 GHz and 5.4 GHz. Furthermore, the specific absorption rate (SAR) analysis of the MIMO antenna offers a range inside of the standard values, showing its potential for On/Off body communications. The comparison with already published works shows that the proposed antenna achieves better results in either compact size or wide operational bandwidth along with low mutual coupling.

Biomass-derived mulberry porous carbon for microwave absorption

In modern electronic devices and communication systems, there is an urgent need for economically viable, environmentally friendly, and efficient absorbing materials. In this work, we have successfully synthesized nitrogen-potassium doped mulberry porous carbon (MBPC) using a one-step pyrolysis strategy for the first time utilizing mulberry as a biomass carbon wave-absorbing material. The resulting MBPC exhibited a porous structure with abundant defects, facilitating the formation of a micro-conductive network and multiple reflection and scattering phenomena, thereby enabling strong microwave absorption. Experimental results demonstrated that the MBPC material achieved a remarkable minimum reflection loss of −44.82 dB at a frequency of 14.64 GHz, along with a maximum effective absorption bandwidth exceeding 5.76 GHz, all within a thickness of only 2.2 mm. Furthermore, by adjusting the thickness between 1 ∼ 3 mm, the bandwidth coverage can be extended to 8.48 ∼ 18 GHz. Computer simulation technology (CST) revealed a high simulated RCS value of 20.68 dB·m2, highlighting its significant practical application potential. This research underscores the promising prospects of mulberry-derived porous carbon as a lightweight, cost-effective, and efficient electromagnetic absorption material.

FPGA implementation of DTCWT architecture's high-speed DA structure for OFDM-based transceiver with CS

Communication systems at millimeter-wave (mm-wave) frequencies with high propagation losses use radio frequency (RF) budget analysis. RF system gains and losses ensure the receiver can recover the broadcast signal. Modern communication systems use compressive sensing (CS) and discrete wavelet transform (DWT). Hardware implementation is hard. Fieldprogrammable gate arrays (FPGA) adaptability, configurability, and processing speed make them popular. More mm-wave transceivers use FPGAs and advanced signal processing. FPGA-based mm-wave transceivers use compressed sensing and dual-tree complex wavelet transform (DTCWT). RF budget analysis recovers receiver signals. Energy and data efficiency transceivers have baseband processors, transmitters, and receivers. RF-to-mm-wave transmitter. Receiver demodulation and baseband conversion. CS and DTCWT processing modules boost baseband signal processing 5 Gbps Xilinx virtex-6 FPGAs. The system retrieves the signal while conserving power, according to simulations and testing. This study found that FPGA-based mm-wave transceivers can use advanced signal processing in future high-speed communication systems.