Wireless Network System Research Articles

Development of Enriched ROA With Dilated Hybrid Network for Automatic Modulation Classification Framework in CRNs

ABSTRACTAutomatic modulation classification (AMC) is explained as accurately identifying a modulation of a received signal. AMC systems are a significant component of cognitive radio network (CRN) systems. It is difficult to perform modulation classification on an unsettled radio signal without any previous knowledge of the signal's properties. In this work, the deep learning‐aided AMC is suggested to solve the difficulties of the existing models. In the proposed approach, the modulation classification is attained by performing two steps: (a) data collection and (b) classification. Initially, the required data related to the cognitive environment is collected from online resources. Later, the garnered data are passed to the classification phase. The AMC is performed by the adaptive and dilated hybrid network (ADHN), which is the combination of a temporal convolution network (TCN) and a gated recurrent unit (GRU). The ADHN accurately classifies the modulation even in a noisy environment. The classification performance of the ADHN is further boosted by tuning the parameters of this network via the enriched remora optimization algorithm (EROA). This proposed modulation classification model is suitable for various channels. The comparative validation is performed to ensure the usefulness of the designed system via several measures. By experimental analysis, the proposed system acquires the high value of accuracy, precision, and f1‐score by 94.2, 80.2, and 86.7, respectively, when compared with classical approaches. In addition to this, other metrics are considered and obtained with more true value and less false value. Thus, it ensures the effectiveness of classifying the modulation types in CRNs.

Secure Data Aggregation and Transmission System for Wireless Body Area Networks Using Twofish Symmetric Key Generation

Nowadays, Wireless Body Area Networks (WBANs) are mostly used in the healthcare industry. They represent a portable, inexpensive network that exhibition adaptability. The data developed using WBAN devices is vulnerable to transmission-related internal and external attacks; nevertheless, this vulnerability arises due to resource restrictions; by employing data aggregation technologies to conduct statistical analyses of medical data while protecting patient privacy, medical professionals can enhance the precision of diagnoses and assist medical insurance firms in selecting optimal plans for their clients. Maintaining the confidentiality and integrity of sensitive health information becomes more stimulating at the stages of aggregation and transmission due to security issues. This study proposes a novel method, Twofish Symmetric Key Generation (TFSKG), combined into a Secure Data Aggregation (SDA) and transmission system intended for WBANs. The Twofish technique is animatedly employed to make the secure symmetric keys chosen for its robust encryption capabilities. These keys are used to encrypt and decrypt aggregated health data through transmission. The proposed TFSKG-SDA method implements effective algorithms for aggregating data to safeguard end-to-end privacy and preserve data accuracy while reducing bandwidth consumption. Thus, for improved performance, an innovative genetic algorithm for data security is presented in this study. This paper introduces TFSKG-SDA, a system that, by employing rigorous simulation testing, enhances security protocols, resistance against recognized threats, and data transmission efficacy in the context of resource-constrained WBANs. We assess the encryption strength, computational cost, and communication efficiency of the TFSKG- SDA method to prove its significance to real-world healthcare applications.

Satellite Onboard Transmitter Design With Spread Spectrum MIMO Antenna for 5G Wireless Networks

AbstractThe 5G new era implements standalone satellite communications that support wireless networking systems for future mobile communications by locating multiple satellites in low Earth orbit to provide global coverage of the entire Earth's surface. In this research, a newly found model of a satellite onboard transmitter using a uniform circular array multiple‐input multiple‐output antenna was designed to operate at a carrier frequency of 12 GHz and derived theoretical equations compared to the real‐time scenario. The integration of spread spectrum with multiple‐input multiple‐output antenna provides an advantage for higher capacity. It has a higher percentage of gain amplification on improving the transmission of electromagnetic power to meet the bandwidth requirement of center operating frequency, and this can transmit over a bandwidth of 1.28 GHz. The proposed satellite onboard transmitter model design aims to minimize the components, increase the speed of operations for higher bandwidth, and transmit large amounts of information to a large group of users. The transmitter can operate for the speed of 1.28 Gbps using pseudo‐random code, direct‐sequence spread spectrum, quadrature phase shift keying modulation, bandwidth separated in bands for 64 symbols using 128 Chebyshev‐type bandpass filter for transmission using 128‐element uniform circular array multiple‐input multiple‐output antenna. The satellite transmitter antenna produces a maximum gain of 14.526 dBi, and a maximum directivity of 17.986 dBi, and the efficiency at 12 GHz is 45.1% for the radiated power at 0.93 mW. This satellite transmitter will interconnect 5G wireless networks for the application of mobile communications complement terrestrial‐dependent networks.

A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network

A Novel Deep Learning Framework for Intrusion Detection Systems in Wireless Network

Dynamic RIS partitioning in NOMA systems using deep reinforcement learning

The rapid evolution of wireless communication technologies necessitates innovative solutions to meet the increasing performance requirements of future networks, particularly in terms of spectral efficiency, energy efficiency, and computational efficiency. Reconfigurable Intelligent Surfaces (RIS) and Non-Orthogonal Multiple Access (NOMA) are emerging as promising technologies to enhance wireless communication systems. This paper explores the dynamic partitioning of RIS elements in NOMA systems using Deep Reinforcement Learning (DRL) to optimize resource allocation and overall system performance. We propose a novel DRL-based framework that dynamically adjusts the partitioning of RIS elements to maximize the achievable sum rate and ensure fair resource distribution among users. Our architecture leverages the flexibility of RIS to create an intelligent radio environment, while NOMA enhances spectral efficiency. The DRL model is trained online, adapting to real-time changes in the communication environment. Empirical results demonstrate that our approach closely approximates the performance of the optimal iterative algorithm (exhaustive search) while reducing computational time by up to 90 percent. Furthermore, our method eliminates the need for an offline training phase, providing a significant advantage in dynamic environments by removing the requirement for retraining with every environmental change. These findings highlight the potential of DRL-based dynamic partitioning as a viable solution for optimizing RIS-aided NOMA systems in future wireless networks.

Multilayer-laminated optically transparent 300-GHz-band transmissive beamforming metasurface for wireless communication

A 300-GHz-band transmission-type multilayer metasurface beamformer based on a Jerusalem cross geometry is presented. A metamaterial cell with a continuous 2π phase variation for the radius change was designed and used in the fabrication of a reference device and three types of beamforming devices. The beamforming metasurface devices achieved steering angles of θ = 18°, θ = 30°, and θ = 38°, which were in good agreement with simulated results. A broadband operation in a 40 GHz band, from 280 GHz to 320 GHz was evaluated, showing only minor frequency dependence of the beamforming angles. The design and fabrication methodology can be applied to various types of metasurface devices, such as circular and fan lenses, beamformers, polarization switches, and so on for millimeter-wave frequency bands that are considered to be used in future 6 G wireless network systems.

A fast and effective approach for microstrip filter design using GA and TL-model

Microwaves and RF technology and their components like filters, antennas, etc. are commonly used in wireless networking and communication systems, wireless security systems, radar systems, and environmental remote sensing. In this paper, a fast and effective procedure has been proposed for microstrip filter design using a genetic algorithm (GA) with a transmission line model (TL). GA is modified to be highly efficient and accurate by encoding the topology. For the fixed filter topology, the electrical parameters of the filter are encoded in a single chromosome. To make the proposed procedure fast and effective, a transmission line model has been employed to compute the fitness value in GA. To demonstrate the effectiveness of the proposed procedure, a wideband second-order bandpass filter with a center frequency of 2.3 GHz is examined with a pair of short-circuited stubs and a pair of open-circuited stubs. The optimized design is validated using full-wave methods (MoM and EM simulator CST). The results show a low insertion loss of 0.1 dB and return loss better than 30 dB and a wide bandwidth of 46.95% with − 3 dB cutoff frequencies at 1.76 GHz and 2.84 GHz.

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

SummaryThe emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.

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.

Decision Making to Choose Communication Network System for Teleremote Dozer Operation Using Analytic Hierarchy Process

One type of heavy equipment is a mining material pushing tool called a dozer. Dozers generally work in areas near cliffs that are prone to landslides, where these areas can be classified as dangerous areas. New technology is needed to increase the safety of dozer operators from the threat of danger when operating a dozer. Teleremote dozers are one method that can reduce the risk of accidents that can happen to dozer operators. Instead of operating the dozer from inside the cabin, the operator operates the dozer via a remote control device. There is an important aspect in operating a teleremote dozer, namely the need for a signal that will transmit data and commands from the remote control to the dozer unit operating in the field. Good and uninterrupted signal quality is the main key to good teleremote dozer operations with minimal risk. The wireless signal network system currently used by companies will enter its obsolete period. However, the old system was tough and not easily damaged. There are strategic options for supporting the signal network for teleremote dozer operations: maintaining the old network system, replacing it with a new one, or using the old and new systems in a hybrid manner. Because there will only be one strategy chosen, a decision must be made. The SWOT AHP method is used for decision making regarding the strategy to be taken. The results of the AHP SWOT will produce a strategy, which will become a benchmark for making subsequent alternatives. Determining alternative types of network systems to support teleremote dozer operations will use the AHP method.

Performance of hybrid transmission in a reconfigurable intelligent surface assisted D2D using NOMA

Recent research on Reconfigurable Intelligent Surface (RIS) aided communication and Non-Orthogonal Multiple Access (NOMA) technology shows enhanced spectral efficiency for 5 G and beyond wireless network systems. This paper evaluates the performance of a Device-to-Device (D2D) network where RIS is deployed in a suitable position and NOMA is employed to accommodate more number of users in the spectrum simultaneously. The D2D users exploit cellular network resources. Analytical models for outage, throughput, and energy-efficiency are developed considering spectrum sensing and a transmit power constraint. This work investigates the network performance in underlay, overlay, as well as hybrid modes through extensive simulations. The simulation model is designed based on the analytical models. Outage probability, network throughput, and energy efficiency are analysed to evaluate the D2D network performance. The simulation results shows incorporation of RIS and NOMA, network performance improves substantially. Increase in number of RIS elements also improves the network performance.

IoT driven joint compressed sensing and shallow learning approach for ECG signal-reconstruction

Because biological signal transmission in real time might be very demanding, cloud and internet of things (IoT) infrastructure are required. To do this, the main component of the signal serves as the focal point of a reconstruction strategy that has been developed. The input is transferred to the intended destination once it has been encoded. Security is an important consideration that must not be disregarded. For long-term healthcare monitoring via lightweight wireless networks, electrocardiogram (ECG) compression is a major difficulty. Reducing energy consumption in wireless data transmission and precisely calculating error rates for data reconstruction are two essential components of compressed sensing. The application of effective encoding methods is crucial for these considerations. We present multi-task compressed sensing (MT-CS), a unique method for compressing ECG data. When used to wireless network systems with several embedded sensors, this technique is quite effective. From the ECG data, the model learns the fundamental adaptive properties needed for correlation. We use the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset to investigate the performance of the suggested MT-CS reconstruction technique in order to assess its strength and application. In comparison to current compressed sensing methods, the simulation results demonstrate that the suggested reconstruction methodology utilizing MT-CS generates high-quality reconstruction signals with fewer observations.

Outage probability of cognitive radio-NOMA assisted unmanned aerial vehicles network

Unmanned aerial vehicles have been applied and play a critical role in radio surveillance because of their flexibility, mobility, and likely line-of-sight to ground destinations. Here, UAVs provide LoS links to users in the communication shadow of obstructive buildings. This paper investigates a cognitive radio network system model between unmanned aerial vehicles to transfer information from a licensed primary device to unlicensed users in a secondary network. In which PN users rely on non-orthogonal multiple access to prevent interference with each other. The primary objective of this study is to examine the outage probability of the secondary users with perfect and imperfect successive interference cancellation applications. Finally, we derive the users’ exact closed-form expressions and use Monte Carlo simulations to validate the analytical results.

A Tutorial on Cross-layer Optimization Wireless Network System Using TOPSIS Method

Wireless network system. Introduction: The availability and rising demand of ubiquitous connectivity has been one of the most revolutionary technological trends of the last ten years. Today, we anticipate being able can access these internet services at any time, from everywhere, for a variety of purposes, including checking email, using the phone, browsing the web, and a plethora of other uses, such as when travelling by car, subway, airline, or anywhere else. Although we still need to actively seek out connections today (by looking for a Wi-Fi hotspot nearby), the future will undoubtedly feature ubiquitous connectivity and Internet access. Central to this movement are wireless networks. A wireless network is any network that is not linked by cables and provides the user with the comfort and mobility they need. We may anticipate that dozens of various wireless technologies will be available to satisfy the demands, each with distinct performance requirements and each tailored for a particular task and environment, given the enormous variety of use cases and applications. Research signification: The densest wireless network deployments up until recently took place in campus-like settings, with the cell architecture being meticulously planned by professionals. The wireless environment is evolving quickly due to the increasing adoption of low-cost 802.11 equipment as well as other personal wireless devices (2.4 GHz cordless telephones, Bluetooth devices). There are two significant differences between dense installations of wireless networking hardware in locations like neighborhoods, shopping centers, and apartment buildings and campus-type deployments. First, numerous separate people or devices, each installing one or a small number of APs, result in complicated installations that are meticulously planned to enhance coverage and reduce cell overlap. The concentration of wireless sensor nodes and APs produced by this kind of haphazard, unplanned deployment is widely varied (and sometimes quite high). Methodology: Alternative: 1G, 2G, 3G, and 4G. Evaluation Preference: Multiplexing, Implementation, Services, Standards, Core Network. Results: from the result it is seen that Core Network is got the first rank where as is the Services is having the lowest rank. Conclusion: The value of the dataset for Wireless network system in TOPSIS method shows that it results in Core Network and top ranking.

Deep Learning for Wireless-Networked Systems: A Joint Estimation-Control–Scheduling Approach

Deep Learning for Wireless-Networked Systems: A Joint Estimation-Control–Scheduling Approach

Analysis of a Fuzzy Based Intrusion Detection System in Wireless Ad Hoc Networks

Technology and its growth is considerably enormous. This massive growth allows the opening of new fields of application in the domain of wireless networking and mobile ad-hoc networks (MANET) is one of its kinds. Mobile ad hoc network is widely used from collaborative computing to time critical applications in indoor and outdoor environment. Mobility of ad hoc network makes very attractive in all areas of mobile applications. Nodes participating in mobile ad hoc networks are autonomous, self-configurable and act as a router as well. These types of networks are dynamic in nature and have sort life time. Dynamic nature and limitations of the wireless transmission medium make MANET unsecured and vulnerable to various attacks. It is very tough to implement security for this networks and it opens up doors for further research work on this area. There is a great scope for designing a system to identify attacks and take countermeasures to minimize it and keep the performance of the network within acceptable limits. Intrusion detection system is one of its kinds and considered as security mechanism for MANET. This thesis explores different types of intrusion detection system like misuse detection and anomaly detection for mobile ad hoc networks. Anomaly detection techniques for ad hoc networks depend on the characterization of normal behavior pattern of wireless nodes. This research work focuses on wireless node behavior based detection technique. Most of anomaly intrusion detection systems are focusing on upper layer traffic to a profile normal behavior of wireless node. This research work focus on media access control (MAC) layer and network layer of wireless node. It is inefficient to use a large feature set of MAC layer and network layer due to energy limitation in ad-hoc network. A minimal feature set from MAC layer and network layer were proposed. This research work proposed an anomaly intrusion detection system for mobile ad hoc network using fuzzy logic and weighted average method. The network performance of mobile ad hoc network with intrusion detection system was analyzed using various network parameters. Further the performance of the performance of the intrusion detection system was analyzed using detection rate and false alarms. Results show good improvement in detection rate and other performance metrics

Design and use of a wireless temperature measurement network system integrating artificial intelligence and blockchain in electrical power engineering.

This work aims to investigate the potential fire hazard stemming from the overheating of power equipment. The advent of the artificial intelligence era has facilitated the fusion of blockchain and Internet of Things (IoT) technologies. This work delves into the technical standards for IoT equipment monitoring and smart grid communication, and the IoT environment of power grid equipment. This work introduces a temperature monitoring network tailored for IoT wireless power equipment suitable for the power environment, and conducts system debugging in the power laboratory. The findings affirm that the temperature out-of-limit alarm testing has met the required criteria, confirming the system's ability to issue timely warnings when temperatures breach a predefined threshold, effectively avoiding high-temperature misfires. This work fully harnesses the secure and user-friendly operation of smart blockchain and the wireless sensing technology of the IoT to realize online monitoring and remote temperature measurement of the power system. It can effectively prevent equipment from overheating and damage, and promote the development of equipment condition monitoring technology in electric power engineering.

Additively Manufactured Semipassive Rotman Lens-Based RIS-Assisted mmID Tag System for Beyond-5G Wireless Networks in Complex Environments

Additively Manufactured Semipassive Rotman Lens-Based RIS-Assisted mmID Tag System for Beyond-5G Wireless Networks in Complex Environments

Biomedical signal compression using deep learning based multi-task compressed sensing

<span>Real-time transmission of biomedical signals is immensely challenging and requires cloud and internet of things (IoT) infrastructure. Security is also an important factor; however, to accomplish this, a reconstruction method is developed in which the entire signal is supplied as an input, the primary portion is considered here, and the signal is further encoded and transmitted to the destination. Electrocardiogram (ECG) compression for the lightweight wireless network is quite challenging for long-term healthcare monitoring. Compressed sensing (CS) involves efficient encoding mechanisms for error rate estimation for reconstruction and energy consumption for wireless transmission of data. We propose a multi-task compressed sensing (MT-CS) reconstruction mechanism in this study for ECG compression of data is most chosen for a wireless network system that has various sensors embedded in it. This model further extracts the essential adaptive features for correlation existing in the ECG signals. The performance of the proposed MT-CS reconstruction mechanism is evaluated on the multiparameter intelligent monitoring in intensive care (MIMIC-II) dataset, which ensures its robustness and generalization. The results obtained upon simulation ensure that the proposed MT-CS based reconstruction approach ensures excellent reconstruction signal with fewer measurements in comparison with the existing state-of-art CS techniques.</span>

Performance Evaluation of OTFS-NOMA Scheme for High Mobility Users

Orthogonal Time Frequency Space (OTFS) is a promising approach which is widely employed in sixth generation (6G) wireless network systems. Because of its superior performance in high-mobility environments, OTFS modulation has received a lot of attention lately. Due to OTFS modulation works in the delay-Doppler (DD) domain rather than the conventional time-frequency (TF) domain, it works effectively in such circumstances. The idea of non-orthogonal multiple access (NOMA) is integrated into OTFS as an important approach to improve the spectral efficiency (SE) to investigate the efficiency potential and performance. In this research, we study OTFS modulated NOMA system for two destination users in the high mobility environment. The message passing detection algorithm is utilized to examine bit error rate (BER) performance for both near and far users in the proposed OTFS modulated NOMA system. The BER simulation results demonstrate that the power allocation (PA) coefficient, delays, and Doppler effects significantly impact the performance of the system.