Wireless Communication Research Articles

Complex-Network based model for SMS spam filtering

With the advancement of technology and the widespread use of mobile phones and wireless communication, SMS has become the most popular texting method due to its high response rate, affordability, and no internet connection requirement. A survey found that 3.5 billion users, or 80% of active users worldwide, use SMS for communication. SMS, however, has also attracted spammers, resulting in an explosion in spam messages, especially in Asia. Users are annoyed, lose money, and waste their time by receiving spam messages intended to serve various purposes, such as advertising, adult content, smishing, and fraud. Spam messages are a problem for users and providers, which calls for a mechanism to identify and filter them out. With supervised machine learning techniques, we propose a novel approach to classify spam and ham messages based on complex network theory. The proposed approach integrates complex network based features with statistical TF-IDF and grammatical rules features. Also, an under-sampling method has been employed in order to cope with the imbalanced data issue. We evaluated the performance of several supervised learners in terms of accuracy, precision, recall, F1-score, and AUC. In our experiments, Random Forest successfully classified spam messages more accurate than statistical methods that only extracted TF-IDF features.

Fault-tolerant topology construction and down-link rate maximization in air-ground integrated networks

Due to the advantages in flexible deployment, fast communication recovery and high quality of service (QoS), unmanned aerial vehicles (UAVs) have been widely used in wireless communications and networking. However, existing works have not sufficiently solved the problem of reliable and survivable connectivity of UAV networks. In this paper, we investigate the deployment of multiple UAVs, cooperating with a ground mobile station (GMS), to provide wireless coverage for ground users. Under the constraints of satisfying different QoS of users and the resource limitation of UAVs, our aim is to minimize the number of deployed UAVs and maximize the sum down-link rate of all users. In order to ensure the survivability of the deployed UAV networks, we also constraint that each UAV has at least two disjoint paths to GMS to form a fault-tolerant topology. Unfortunately, the formulate problem is intractable and cannot be solved directly. To solve this problem, we decouple the original problem into two subproblems: (1) minimized fault-tolerant topology construction concerning user demands; (2) maximized down-link rate with access policy optimization. Then, we propose a heuristic link-cost minimization method and a potential-game-based user rate maximization method to solve the two subproblems, respectively. The effectiveness of the solution is validated through simulations. Meanwhile, ours also outperforms some baselines in minimized UAVs, down-link transmission rate as well as running time.

Sub-1 GHz RF-based Energy-efficient Sensor Node for Secure Communication in Low-power IoT and Embedded Applications

Background: The Internet of Things (IoT) devices consist of a microcontroller unit for data processing, a low-power wireless radio module for data transmission, and various sensors for data collection. The sensor nodes and processing devices used in the Internet of Things are resource-constrained, with power consumption and security being the two most critical parameters. Objective: This paper addresses the challenges of power consumption and security in IoT scenarios. It presents a low-power and secure heterogeneous multicore sensing architecture designed for low-power IoT and wireless sensor networks. The architecture comprises a sensing and control subsystem, an information processing unit, and a wireless communication module. Methods: The architecture uses a microcontroller unit based on ARM Cortex M4, a low-power sub-1 GHz RF-compliant communication radio, and a few sensors. The proposed architecture has been implemented and tested using the Contiki Operating System. Results: The implemented sensor node architecture demonstrated performance efficiency, lower energy consumption, and higher security. Conclusion: By leveraging efficient power management, data transmission strategies, and cryptographic security, the architecture contributes to developing energy-efficient and secure IoT devices.

Gilbert cell down-conversion mixer for THz wireless communication with passive baluns

Gilbert cell down-conversion mixer for THz wireless communication with passive baluns

Design of Low Noise Amplifier Based on Tuneable Active Inductor and Capacitor for Bluetooth Applications

Active components such as active inductors and active capacitors have been used to replace their passive counterparts, especially in radio frequency modules. Besides saving colossal silicon area, active inductors are power saviors, tuneable, and provide large inductance ranges. This paper presents the evaluation and performance of a low noise amplifier (LNA) implemented using only active components. The active inductor has a high inductance tunability range from 848 nH to 1660 nH, resulting in a frequency range from 1.85 GHz to 3.02 GHz. The active inductor's quality factor is 11, with a maximum power dissipation of 208.1 µW from a 0.5 V DC power supply. The LNA provides a voltage gain of 8.76 dB with a noise figure of 8.21 dB while consuming 779.16 µW from a 0.85 V supply. The achieved frequency range is commonly used for various wireless communication applications, including Wi-Fi and Bluetooth.

Enhanced Security Framework for Multi-Layered Wireless Communication Systems

The goal is to predict a Windows machine’s probability of getting infected by various families of malware, based on different properties of that machine. The telemetry data containing these properties and the machine infections was generated by combining heartbeat and threat reports collected by Microsoft’s endpoint protection solution, Windows Defender. Each row in this dataset corresponds to a ma- chine, uniquely identified by a Machine Identifier. Has Detections is the ground truth and indicates that Malware was detected on the machine. Using the information and labels in train.csv, you must predict the value for Has Detections for each machine in test.csv. The sampling methodology used to create this dataset was designed to meet certain business constraints, both in regards to user privacy as well as the time period during which the machine was running. Malware detection is inherently a time-series problem, but it is made complicated by the introduction of new machines, machines that come online and offline, machines that receive patches, machines that receive new operating systems, etc. While the dataset provided here has been roughly split by time, the complications and sampling requirements mentioned above may mean you may see imperfect agreement between your cross validation, public, and private scores! Additionally, this dataset is not representative of Microsoft customers machines in the wild it has been sampled to include a much larger proportion of malware machines

Machine learning-based models for forecasting radio refractivity over the coastal area of South Africa

Surface refractivity is a crucial parameter that determines the bending of radio signals as they propagate within the troposphere. It is greatly influenced by the atmospheric weather conditions and changes rapidly, especially in the coastal areas. This research utilized 50 years (1974-2023) surface temperature, pressure, and humidity data from six coastal stations in South Africa to forecast radio refractivity in the Mediterranean climate. Five machine learning models: Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LightGBM), Long-Short Term Memory (LSTM), Prophet, and Random Forest were trained for future prediction of surface refractivity at any coastal area in South Africa. The stations latitude, longitude, altitude, surface refractivity and date were applied as the input parameters to train the models. The models were optimized through the randomized searchCV hyperparameter tuning to improve their efficiency. The LightGBM outperformed other models with RMSE and adjusted determination coefficients of 1.67 and 0.96, respectively. The model is recommended for future prediction of surface refractivity needed for the improvement of point-to-point wireless communication, terrestrial radio and television transmissions, and mobile communication networks in the coastal sub-tropical regions.

ChessCrypt: enhancing wireless communication security in smart cities through dynamically generated S-Box with chess-based nonlinearity.

In the contemporary landscape of smart cities, ensuring the security and confidentiality of transmitted data has become paramount. The proliferation of Internet of Things (IoT) devices, mobile communication platforms, and wireless sensor networks has magnified the need for robust cryptographic solutions to safeguard sensitive information from malicious adversaries. In response to these challenges, we introduce ChessCrypt-a novel approach to enhancing wireless communication security through the development of a new S-Box algorithm inspired by the nonlinear movement patterns of chess pieces. ChessCrypt leverages the dynamic and unpredictable nature of chess piece movements, namely knights, kings, and bishops, to introduce a high degree of nonlinearity and confusion into the encryption process. By incorporating elements of randomness and unpredictability, our proposed S-Box algorithm offers robust protection against a wide range of cyber threats, including eavesdropping, data interception, and cryptographic attacks. We demonstrate the effectiveness and resilience of ChessCrypt through rigorous testing and analysis, showcasing its superiority over traditional cryptographic methods in enhancing the security of wireless communication networks. Our results underscore the significance of ChessCrypt as a promising solution for fortifying wireless communication security in an increasingly interconnected world of ours.

Design and experimental demonstration of functional devices for energy manipulation in new analog acoustic computers

The development of new analog computers based on analog signals of electromagnetic or elastic waves has become a hot research topic in recent years. In this Letter, based on the concept of quantum state transfer, we study the process of energy transfer between acoustic cavities. Through the transformation from the temporal domain to the spatial domain, we further study the process of energy transfer between acoustic waveguides, and then we realize the applications of energy distributors and limiters in the acoustic system, which are the functional devices of wireless communication technology in new analog acoustic computers. We both simulationlly and experimentally demonstrate the effectiveness of the two applications. The energy distributor and limiter we proposed have the advantages of a relatively wide operating frequency range and adjustability. Our work shows the transfer mechanism of acoustic energy between different structures, which increases its potential application value in new analog acoustic computers.

Enhancing Subsurface CO₂ Storage Monitoring and Sensing: A Novel Long-Term, Real-Time Wireless Communication System

Enhancing Subsurface CO₂ Storage Monitoring and Sensing: A Novel Long-Term, Real-Time Wireless Communication System

Method for determining the effect of destabilizing factors of digital filters on the multicriteria synthesis of radio communications of aviation robotic devices

The purpose of the research is development of a method for determining the influence of destabilizing factors, such as the unevenness of the amplitude-frequency response (AFC) and the nonlinearity of the phase-frequency response (FFC), in non-recursive digital frequency selection filters on the target function of multicriteria structural-parametric synthesis of radio communications from the payload of aviation robotic devices.Methods. In conducting scientific research, probability theory, mathematical statistics, statistical radio engineering and computational mathematics were used. The study obtained mathematical expressions of the dependence of the probability of error per bit (BER) on the average signal-to-noise ratio when signals pass through symmetrical non-recursive digital filters with quadrature amplitude modulation with a positivity of no more than 4096, signal constellation irregularities of no more than 5, which are the basis for the energy balance of radio lines with aviation robotic devices. The expressions obtained are one of the "foundations" for the implementation of a multi-criteria synthesis of radio systems adaptive to a cognitively formed flight task. In this paper, all the results of the probability of reliability of the received data by means of communication are reflected for the case of rigid decision-making (Gray coding).Results. In the course of the conducted research: 1) a mathematical model of the structural-parametric synthesis of radio communications with aviation robotic devices is demonstrated, which is based on the criteria for minimizing: electronic computing resources with a given noise immunity; radiation outside the operating frequency band; peak factor signals; the effects of narrowband, broadband and structural interference in the radio channel at a given bandwidth; the use of structural and functional nodes specific to the existing information and communication environment; 2) the analytical dependence of the influence of the parameters of one of several structural and functional nodes - digital filters of frequency selection, on the criteria for the synthesis of communication means has been revealed. The paper considers the most common approximating functions - Kaiser, Tukey, Barlett-Khan, Chebyshev, and also shows the influence of the filter order on the equivalent of energy loss in information communication lines of aviation robotic devices.Conclusion. The scientific article presents a method for determining the effect of the uneven frequency response of a symmetrical non-recursive digital filter in devices for receiving and processing radio signals of multicriteria means of communication with the payload of aviation robotic devices. The proposed approach increases the reliability of determining the values of criteria for minimizing electronic computing resources for a given noise immunity, radiation outside the operating frequency band, as well as the use of structural and functional nodes characteristic of the existing information and communication environment.

6G secure wireless communications using AI-based federated learning

6G secure wireless communications using AI-based federated learning

On the posterior property of the Rician distribution

The Rician distribution, a well-known statistical distribution frequently encountered in fields like magnetic resonance imaging and wireless communications, is particularly useful for describing many real phenomena such as signal process data. In this paper, we introduce objective Bayesian inference for the Rician distribution parameters, specifically the Jeffreys rule and Jeffreys prior are derived. We proved that the obtained posterior for the first priors led to an improper posterior while the Jeffreys prior led to a proper distribution. To evaluate the effectiveness of our proposed Bayesian estimation method, we perform extensive numerical simulations and compare the results with those obtained from traditional moment-based and maximum likelihood estimators. Our simulations illustrate that the Bayesian estimators derived from the Jeffreys prior provide nearly unbiased estimates, showcasing the advantages of our approach over classical techniques. Additionally, our framework incorporates the S.A.F.E. principles – Sustainable, Accurate, Fair, and Explainable – ensuring robustness, fairness, and transparency in predictive modelling.

Evaluation of BLE Star Network for Wireless Wearable Prosthesis/Orthosis Controller

Concomitant improvements in wireless communication and sensor technologies have increased capabilities of wearable biosensors. These improvements have not transferred to wireless prosthesis/orthosis controllers, in part due to strict latency and power consumption requirements. We used a Bluetooth Low Energy 5.3 (BLE) network to study the influence of the connection interval (10–100 ms) and event length (2500–7500 μs), ranges appropriate for real-time myoelectric prosthesis/orthosis control on the maximum network size, power consumption, and latency. The number of connections increased from 4 to 12 as the connection interval increased from 10 to 50 ms (event length of 2500 μs). For connection intervals ≤50 ms, the number of connections reduced by ≥50% with the increasing event length. At a connection interval of 100 ms, little change was observed in the number of connections vs. event length. Across event lengths, increasing the connection interval from 10 to 100 ms decreased the average power consumed by approximately 16%. Latency measurements showed that an average of one connection interval (maximum of just over two) elapses between the application of the signal at the peripheral node ADC input and its detection on the central node. Overall, reducing the latency using shorter connection intervals reduces the maximum number of connections and increases power consumption.

Low-profile pattern reconfigurable patch antenna with dual bands characteristic for 5G NR communication

Abstract In this paper, a low-profile antenna with characteristic of reconfigurable of dual bands’ pattern for 5G New Radio (NR) communication is proposed to mitigate the problems of signal fading and multipath propagation. The design incorporates a turnstile-shaped patch, a circular patch with etched rectangular slots and a discontinuous ring patch. The circular patch with etched rectangular slots and discontinuous ring patch are placed in the same plane to obtain dual band characteristic. The discontinuous ring patch works as a near-field resonant parasitic unit to improve the front-to-back ratio values at the resonance points and adjust the beam direction. Eight diodes are loaded to control the connection states of the rectangle slots on the circular patch. By combining an electric dipole formed by diode-controlled slot with a magnetic dipole formed by the turnstile-shaped patch, then a dual-band antenna with diverse patterns is designed. The measured results show that the designed antenna has dual-band characteristics and operates in the bands of 3.39–3.62 GHz and 4.77–5.01 GHz with peak gains of 3.6 dBi and 4.2 dBi, respectively. Furthermore, the measured radiation pattern results show that it is feasible to reconfigure the pattern in both bands simultaneously at 45° intervals.

Retraction Note: Optical wireless communication based mobile edge computing integrated channel allocation using scheduling with machine learning protocols in advanced 5G networks

Retraction Note: Optical wireless communication based mobile edge computing integrated channel allocation using scheduling with machine learning protocols in advanced 5G networks

Deep learning-based spectrum sensing and modulation categorization for efficient data transmission in cognitive radio

Abstract One prominent feature of cognitive radio (CR) involves spectrum sensing (SS), which allows licensed primary users to remain unaffected by secondary users’ ability to discover and exploit unoccupied frequency bands. Spectrum sensing enhances the use of spectrum in CR devices, increasing their adaptability and efficiency in wireless communication systems. The rise of wireless equipment and the advent of IoT technologies compound this need for flexibility. Over time, the fixed allocation of frequencies has led to inefficiencies and underutilization as bandwidth needs increase. Deep learning and artificial intelligence have improved spectrum sensing by increasing detection probability of primary users’ presence under noisy environments, enabling cognitive radio systems to respond intelligently to fluctuations in RF environments. This article is concerned with deep learning techniques for spectrum sensing and modulation categorization with CBRT structure, which combines convolutional neural networks (CNNs), bidirectional recurrent neural networks (BRNNs), and transformer networks (TNs) to improve spectrum sensing. CNNs are responsible for performing spectrum feature extraction; BRNNs are used to capture temporal dependencies; and TNs are good at long range dependencies. Better performance for this model is aimed by integrating the three architectures described. In the proposed work, six digital modulation schemes were considered, for spectrum sensing. The sensing of spectrum in this model is performed using the RadioML2016.10B open-source dataset and performance metrics like the Jaccard Index (JI), Fowlkes’s Mallows Index, and F1 Score. Modulation classification has been performed using MIGOU-MOD open-source dataset. The proposed model exhibits good detection probability and low sensing error, unlike other methods at lower SNR.

COMMUNICATION MODULES FOR DATA TRANSMISSION IN PHOTOVOLTAIC PERFORMANCE MONITORING AND ANALYSIS SYSTEMS

The paper discusses the wireless communication modules used for monitoring photovoltaic systems-ZigBee, Wi-Fi, Bluetooth, GSM and LoRa. The aim is to compare the main features, advantages and disadvantages of each of these technologies. The analysis identifies the need for reliability, low power consumption and secure data transmission in the design of the systems.

Traffic Awareness-Based Target Wake Time Scheduling in 802.11ax WLANs

In recent years, the rapid growth of mobile communication has led to the continuous expansion of network scale, and the energy consumption of wireless communication has also increased rapidly. Under the high-density networking conditions, frequent communication activities greatly increase the energy consumption. In order to reduce channel competition, optimize resource scheduling and reduce equipment power consumption, the target wake-up time (TWT) mechanism in IEEE 802.11ax system plays an important role through timely scheduling and transmission. To improve channel efficiency for multi-user (MU) transmission and tackle with complicated traffic conditions, this paper presents a traffic awareness-based TWT scheduling scheme (TAT). The traffic is predicted and classified using a spatio-temporal series model for traffic-based TWT parameter generation. Subsequently, the time–frequency scheduling table is developed to adaptively assign Resource Units (RUs) and time slices to active STAs. Simulation results show that TAT can guarantee the Quality of Service (QoS) under dynamic changes in power service and delay requirements while reducing power consumption, effectively meeting the energy-saving demands in the intelligent access scenarios for power terminals.

Design and performance evaluation of a hybrid FSO-FTTx communication link utilizing UD-WDMA 1.28 Tbps data rates transmission under various weather conditions

Abstract The rapid growth of high-data-rate applications necessitates the development of new communication frameworks since bandwidth constraints for data-intensive applications hinder traditional networks and conventional microwave/radio frequency (RF) communications. This study explores a hybrid communication link that combines fiber-to-the-x (FTTx) and free-space optical (FSO) technologies, utilizing ultra-dense wavelength-division multiple access (UD-WDMA) with a channel spacing of 0.2 nm/25 GHz, under various weather conditions. Based on bit error rate (BER), optical signal-to-noise ratio (OSNR), and quality factor (QF), the performance of the suggested FSO-FTTx system was examined. The system performed well in diverse weather conditions, achieving a minimum BER of ≤10−5, QF values of 4 or higher, and OSNR levels between 10 and 20 dB. The proposed system successfully transmitted a data rate of 1.28 Tbps over 35 km in single-mode fiber (SMF), accommodating different FSO link ranges despite varying weather conditions. However, performance fluctuations were observed under adverse conditions, with attenuation ranging from 0.91 dB/km in extremely light mist to 273.39 dB/km in dense fog, impacting the FSO link range. The findings underscore the proposed hybrid system’s potential to enhance optical wireless communication for high data rates, making it promising for beyond 5G and early 6G applications.