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Wireless Personal Communications
Journal Prestige (SJR): 0.26  Citation Impact (citeScore): 1 Number of Followers: 6  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1572-834X - ISSN (Online) 0929-6212 Published by Springer-Verlag  [2467 journals]
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- Fuzzy-EPO Optimization Technique for Optimised Resource Allocation and
Minimum Energy Consumption with the Brownout Algorithm-
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Abstract: Cloud Computing is an eminent and reputable agenda which relies on large-scale distributed processing to provide access to their resources and services. In the cloud environment a rigorous management system is mandatory to collect all information regarding task processing levels and proving impartial resource provisioning through the levels of Quality of Service (QoS). These concerns can be settled by employing a meta-heuristic optimization-based resource management. Subsequently, this paper presents a Fuzzy Emperor Penguin Optimization (Fuzzy-EPO) algorithm-based resource provisioning framework for heterogeneous cloud environment. To deploy the optimal set of virtual machines (VM) to physical machines the VM allocation model is employed. The proposed Fuzzy-EPO algorithm does the VM consolidation mainly to reallocate overloaded VM to under-loaded PM to minimize the migration time and the brownout mechanism is adopted to reduce the rate of energy consumption. CloudSim simulation platform is used to implement the proposed system. The simulation results expose that the proposed Fuzzy-EPO based system is effective in restraining the proportion of service level agreement violation and increasing QoS requirements for providing proficient cloud service.
PubDate: 2023-03-25
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- ETELMAD: Anomaly Detection Using Enhanced Transient Extreme Machine
Learning System in Wireless Sensor Networks-
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Abstract: Anomaly detection is a major task for ensuring security in WSNs, and they are sensitive to several attacks, which cause the node to break and generate faulty results. This proposed architecture introduces a method named Enhanced Transient Extreme Learning Machine Anomaly Detection to resolve such an issue. The detection of anomalies in the sensor data is recorded in three stages: data compression, prediction, and anomalous detection. The data collected from the network is pre-processed, and the duplicate values are eliminated from the dataset. Piecewise Aggregate Approximation is employed for the data compression process. This method can extract a low-dimensional set of features with less dimension and high accuracy. The reduction in dimensionality plays a major role in the WSN environment and attains less computation or training time. The second phase is prediction, done by an Extreme Learning Machine (ELM). The parameters of ELM are optimized by the meta-heuristic approach Enhanced Transient Search Arithmetic Optimization. Finally, the anomalous data is detected using the dynamic thresholding method. Dynamic thresholding is a process that generates a set of threshold values to differentiate the normal and abnormal sensed data. The PYTHON platform is used to simulate the proposed process. The achieved performance is compared over other models based on some measures to depict the efficacy of the proposed anomaly detection model. The overall accuracy achieved by this proposed architecture for the IBRL dataset is 97.4%, which is more efficient than other existing approaches.
PubDate: 2023-03-25
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- Deep Learning Based Channel Estimation and Secure data Transmission Using
IEHO-DLNN and MECC Algorithm in Mu-MIMO OFDM System-
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Abstract: Channel Estimation (CE) is extremely important in estimating accurately the Channel Impulses Responses (CIR) in disparate conditions. Thus, CE is an extremely vital procedure in the Multiple Users Multiples-Input Multiples-Output–Orthogonal Frequency-Divisions Multiplexing (MU-MIMO-OFDM). However, Inter-Symbols Interferences (ISI) and Inter-Users Interferences (IUI) are the major challenges that the MU-MIMO-OFDM has to face. Obtaining Channel States Information (CSI) is very hard on account of the occurrence of the ISI and IUI in the wireless communication (WC) channel. Conversely, one of the constrictions of MU-MIMO-OFDM is secure signal transmission. To address all these issues, this work proposes a deep learning-centered CE and secures Data Transmission (DT) utilizing IEHO-DCNN and MECC algorithm in the MU-MIMO-OFDM. Initially, the MECC algorithm encrypts the input signals at the transmitter’s side for rendering secure DT. Next, to shun ISI and accurately estimate the CIR, the Cyclic Prefix (CP) along with pilot symbols is interleaved into the signal. The channel is evaluated via IEHO-DLNN. Additionally, the fuzzy-centered priority scheduling is adopted to shun the IUI. It scheduled the manifold users at the received side centred on their waiting time. The proposed work estimates the channel with a minimal cost function, which is experimentally proved via comparing it with prevailing methods.
PubDate: 2023-03-25
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- Design of Linear and Circular Antenna Arrays for Side Lobe Reduction Using
a Novel Modified Sparrow Search Algorithm-
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Abstract: A high gain antenna array with reduced side lobe level (SLL) needs to be optimized and designed to meet the requirement of modern wireless communication systems. To achieve this goal, a newly developed natural heuristic algorithm namely sparrow search algorithm (SSA) and its modification are introduced and utilized to the field of electromagnetic optimization for the first time in this paper. Simulation results over several different examples of the linear antenna array (LAA) and circular antenna array (CAA) design problem have been presented to demonstrate the effectiveness and superiority of the modified SSA. The design results obtained by modified SSA showed greater advantages than those certain classical and well-known algorithms like particle swarm optimization (PSO), whale optimization algorithm (WOA) and grasshopper optimization algorithm (GOA), in a statistically meaningful way.
PubDate: 2023-03-24
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- SOA Based BB84 Protocol for Enhancing Quantum Key Distribution in Cloud
Environment-
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Abstract: Quantum Key Distribution (QKD) systems are thought to be the best method for securing data in cloud storage and boosting security and privacy. Due to the increasing use of cloud services, ensuring the confidentiality of stored data in cloud storage, data exchange, and key sharing used to encrypt data has become a major concern in recent years. The error key may occur during key generation. Through this error key, Eve can easily know the knowledge of the shared key. Enhanced error correction algorithms are utilized to discover and eliminate mistake bits while transmission, ensuring that both keys are equal and producing their shared error-free secret key. Hence, this study improves a BB84 protocol by improving its bit size at the compatibility level using the Sailfish Optimization Algorithm (SOA), and together with the transmitter, as well as the receiver, create a raw key in the next state. QKD is developed from improved BB84 protocol and encrypts data using a hybrid AES-RC4 encryption algorithm. The improved BB84 protocol generates the quantum key distribution, which encrypts data using a hybrid encryption algorithm. Here, error correction is done through the multi-objective function which is optimized using the Sailfish optimization technique, resulting in outcomes through adding either estimate mistake or a best key combination. After encryption, if the data is uploaded to the cloud, only the authorized user can decode the data. Moreover, in a Python environment, the proposed method is implemented, and the proposed model's accuracy rate is 97 per cent, with a 3 per cent error rate and 59 s for key generation time. As a result, the proposed SOA-based QKD swift key generation system outperforms existing methods.
PubDate: 2023-03-24
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- Secrecy Outage of an Energy Harvesting Multi-relay Network with
Multiple-antenna-
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Abstract: Secrecy performance of a cooperative communication network is analyzed in which source transmits information to a destination via multiple relays, assisted with multiple antennas, where an eavesdropper is able to eavesdrop the information signal from relay only. The direct link does not exist between source and destination due to deep fading and shadowing effect and transmission of a signal from source to destination is completed in two phases; first is the broadcasting phase and other is the relaying phase. Each relay follows decode and forward protocol and receives the information signal with multiple antennas. A selected relay harvests energy from received multiple signals through multiple antennas. Both information and jamming signals are transmitted by the selected antenna of a selected relay. It has been seen that a selected relay harvests energy 0.3706 joule with an array of 5 antennas when the source transmits the information signal with 5 dBW power. We have also observed that the performance increased by \(99.24 \%\) in case of an array four antennas system with respect to an array of two antennas system when secrecy threshold is \(1\,bit/s/Hz\) . Secrecy performance is measured in terms of secrecy outage probability (SOP). A closed-form expression of the SOP is derived to analyze the secrecy performance which is verified by the simulation results.
PubDate: 2023-03-23
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- A Comprehensive Study on Smart Agriculture Applications in India
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Abstract: The rampant adoption of digital technologies made momentous changes in all economic sectors. The agriculture sector cannot abstain from the digital revolution. Agriculture and farming are one of the oldest and most important professions in India. The sector remains the backbone of the Indian rural economy, which desperately demands technological impetus for the socio-economic development of rural areas. Smart agriculture is a revolution in the agriculture industry which helps to guide the actions that are required to modify and reorient the agricultural systems. The paper made an extensive survey of various technologies proposed for the agriculture sector. We have surveyed various smart agricultural applications developed and proposed a taxonomy for classifying them. Network infrastructure and connectivity remains the major challenge for rural areas. The paper explores the viability of deploying IoT-based technologies in agricultural sectors along machine learning techniques to optimize resource utilization, planning and cultivation, marketing, pesticide selection, price prediction, etc. An in-depth coverage of recent research works is also mentioned which will help the future researchers to address specific challenge and adopt suitable technology to help the farmers to improve their productivity and better decision making in cultivation. Apart from listing the applications, we also propose an architecture for smart agriculture and implemented smart price prediction model for crops like cotton and cardamom along with a prototype for smart irrigation.
PubDate: 2023-03-23
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- Study of Various Cyber Threats and Their Mitigation Techniques
Requirements-
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Abstract: Now days Targeted Cyber Attacks (TCA) and Advanced Persistent Threats (APT) are the main reason regards many Cyber espionages and sabotages. TCAs and APTs was widely allocated, target particular and execute under soft mode till destination was accepted and are difficult to be identified through traditional security systems. The thinking regards above-mentioned attacks were to execute target particular automated malwares under a host either network. Traditional security technique such as antivirus, anti-malware system that based on signatures and static observation failed to analyze such attacks. Hence there is a need for efficient solutions to detect TCAs and APT. In this dissertation, three novel methods have been proposed to detect such attacks. The first method deals with detecting APTs whereas the second method deals with Intrusion Detection Honey pot (IDH) for detecting and mitigating targeted ransom ware attacks. Finally, a novel Wireless Intrusion Detection System (WIDS) has been proposed to detect targeted attacks using drones.
PubDate: 2023-03-23
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- Nonlinear Energy Harvesting and Clustering Cooperation in WPCNs
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Abstract: The increasing demand for data and the rapid increase in the number of wireless connected devices make the shortage of energy and spectrum resources more serious. This paper considers a wireless powered communication network (WPCN) composed of N wireless devices (WDs) installed with single-antenna and a hybrid access point (HAP) equipped with multi-antenna, where HAP sends wireless energy to WDs in the downlink and receives information transmission from WDs in the uplink. To overcome “double near and far” problem, this paper adopts a clustering cooperative transmission method to enhance some WDs’ throughput performance far from the HAP, i.e., one of N WDs is selected as a cluster head (CH) and the remaining \((N-1)\) WDs as cluster members (CMs), and the CH helps relay CMs’ information to transmit. However, because the CH needs to transmit N WDs’ information, its energy consumed during information transmission will be the bottleneck of the system performance. To achieve a tradeoff between energy and data rate, this paper adopts multi-antenna energy beamforming technology to concentrate more energy to transmit the CH, so as to balance the energy consumption among all WDs. Considering the influence of in-phase/orthogonal imbalance,nonlinear amplification amplitude and phase noise on those physical transceivers of low-cost sensor nodes, nonlinear energy harvesting technology is employed to improve throughput performance of the WPCN system. Particularly, the proposed scheme’s throughput performance is derived, and simulation results demonstrate that this scheme can be effective to increase the WPCN system’s throughput fairness and spectral efficiency.
PubDate: 2023-03-23
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- Critical Comparative Analysis and Recommendation in MAC Protocols for
Wireless Mesh Networks Using Multi-objective Optimization and Statistical
Testing-
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Abstract: Wireless Mesh Network (WMN) is surely one of the prominent networks in the modern era which is widely used in numerous evolving applications, viz. broadband home networking (BHN), community and neighbourhood networks (CNN), coordinated network management (CNM), and intelligent transportation systems (ITS), etc. It is a wireless network (WN) with multi-hop formed by many fixed wireless mesh routers (WMR) that are connected wirelessly with a mesh-alike backbone arrangement. In the IEEE 802.11 s network, the node selection, scalability, stability, density of the nodes, mobility of the nodes, transmission power, and routing are major issues that WMN suffers. In this paper, a critical review of MAC protocols and their Quality of Service (QoS) parameters for WMN is presented to attain a better understanding of MAC protocols. Furthermore, the critical comparative analysis and recommendation of MAC procedures for WMN using Multi-objective optimization and statistical testing framework are performed. This framework is used for the analysis and recommendation of different protocols available for QoS parameters.
PubDate: 2023-03-23
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- Smart Assistance to Reduce the Fear of Falling in Parkinson Patients Using
IoT-
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Abstract: Parkinson’s disease (PD) is often classified as a neuro-degenerative movement disorder that is related with the gait and balance difficulties and a significantly increased danger of falling. Multipart movements, like the turns, may often cause instability in the balance and result in a fall. Falls in PD may necessitate an increased assistance and lead to the higher possibility of hospitalization together with a lethal effect on the lifestyle quality. So, there is a high demand for a fall detection system (FDS) for the PD patients which may assist them to reduce the fear of falling (FoF) and will also improve the patient care services. The threshold based fall detection systems has faster response time and lesser resource requirements compared to the machine learning based design. In this paper, we proposed a smart threshold based FDS with a nominal computational overhead, using an on board tri-axial accelerometer of the smartphone. The proposed FDS improves the overall system accuracy at the time of post fall emergency situation compared to the other traditional fall supervision techniques. The system has 94.45% accuracy and could reduce the FoF of the PD patients upto 10% in some cases.
PubDate: 2023-03-23
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- NaISEP: Neighborhood Aware Clustering Protocol for WSN Assisted IOT
Network for Agricultural Application-
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Abstract: Smart farming is becoming the need of the hour nowadays in an effort to boost productivity and protect the crops. This can be done using sensors and internet enabled devices in the farm. The underlying wireless sensor network can sense various environmental parameters and can pass data to the internet enabled devices to support smart farming. The sensor nodes are, however, powered by smaller batteries and have limited lifetime. Therefore, this paper presents a clustering protocol which aims at increasing the lifetime of the sensor nodes. These sensors are considered to be pressure sensors which are deployed in the network and whenever any animal encroaches the farm, a signal can be passed to the internet enabled alarm system which can help the farmer to fend off the animals and protect his crops. The sensor network is considered to have three level of energy heterogeneity among the nodes and the cluster head is selected in such a way that the cluster formed by the head consists of more number of high energy nodes. The proposed protocol has been simulated in MATLAB environment and compared with Stable Election Protocol (SEP), Distributed Energy Efficient Clustering (DEEC) and Improved Stable Election Protocol (ISEP) based on network lifetime and throughput. The network lifetime for proposed protocol was 6567 rounds which was higher than others with network going dead at 5472 for ISEP, at 2495 for DEEC and at 2316 for SEP. The protocol has shown better performance against these existing protocols.
PubDate: 2023-03-22
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- Deep Recurrent Neural Model for Multi Domain Sentiment Analysis with
Attention Mechanism-
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Abstract: The problem of multi-domain sentiment analysis is complex since meaning of words in different domains can be interpreted differently. This paper proposes a deep bi-directional Recurrent Neural Network based sentiment classification system employing attention mechanism for multi-domain classifications. The approach derives domain representation by extracting features related to description of domain from the text using bidirectional recurrent network with attention and feed it to the sentiment classifier along with the processed text using common hidden layers. We experiment with varied types of recurrent networks and propose that implementing the recurrent network with gated recurrent unit ensures that both domain-specific feature extraction and feature sharing for classification can be performed simultaneously and effectively. The evaluation of domain and sentiment modules has been conducted separately and results are encouraging. We found that using gated recurrent unit as bidirectional recurrent network in both modules gives efficient performance as it trains quickly and gives higher validation accuracy for all present domains. The proposed model also demonstrated good results for other metrics when compared with other similar state-of-the-art approaches.
PubDate: 2023-03-22
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- AI-assisted Emergency Healthcare using Vehicular Network and Support
Vector Machine-
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Abstract: The COVID-19 pandemic has created an emergency across the globe. The number of corona positive and death cases is still rising worldwide. All countries’ governments are taking various steps to control the infection of COVID-19. One step to control the coronavirus’s spreading is to quarantine. But the number of active cases at the quarantine center is increasing daily. Also, the doctors, nurses, and paramedical staff providing service to the people at the quarantine center are getting infected. This demands the automatic and regular monitoring of people at the quarantine center. This paper proposed a novel and automated method for monitoring people at the quarantine center in two phases. These are the health data transmission phase and health data analysis phase. The health data transmission phase proposed a geographic-based routing that involves components like Network-in-box, Roadside-unit, and vehicles. An effective route is determined using route value to transmit data from the quarantine center to the observation center. The route value depends on the factors such as density, shortest path, delay, vehicular data carrying delay, and attenuation. The performance metrics considered for this phase are E2E delay, number of network gaps, and packet delivery ratio, and the proposed work performs better than the existing routing like geographic source routing, anchor-based street traffic aware routing, Peripheral node based GEographic DIstance Routing . The analysis of health data is done at the observation center. In the health data analysis phase, the health data is classified into multi-class using a support vector machine. There are four categories of health data: normal, low-risk, medium-risk, and high-risk. The parameters used to measure the performance of this phase are precision, recall, accuracy, and F-1 score. The overall testing accuracy is found to be 96.8%, demonstrating strong potential for our technique to be adopted in practice.
PubDate: 2023-03-22
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- Design of Smart Antenna for 5G Network Using Array Synthesis Methods and
Leaky LMS Algorithm-
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Abstract: 5G antenna arrays are being designed to generate a dedicated stream of data for every single user. This results in more capacity and speed over the network along with the least possible latency rate. Depending upon the direction of the user demanding internet access, the beam can be steered in that direction. Meanwhile, the occurrence of side lobes in such systems can be menacing. The smart antenna is a pivotal technology for modern cellular communication. It helps the user’s signal to determine the direction of arrival It also estimates antenna arrays using an adaptive signal processing algorithm that produces a radiation beam for communication. This work presents beamforming for uniform linear smart antenna array using leaky least mean square algorithm and side lobe level reduction using array synthesis methods like Tchebycheff and Taylor distribution. Multiple interferers are considered for the smart antenna. Here, one of the aims is to reduce side lobe levels nearer to the main beam which can enhance more efficient frequency reuse in a cellular network. Side lobe level is reduced up to about 16 dB that enacted for signal-to-noise ratio to 20 dB.
PubDate: 2023-03-22
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- Multi-Modal Industrial IoT Networks: Recent Advances and Future Challenges
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Abstract: While the ongoing fourth industrial revolution continues to be a major driver behind wireless communication technologies, some environments are so prohibitive that even state-of-the-art solutions can barely achieve ubiquitous wireless connectivity (if at all). For example, in industrial sites with large metal constructions (such as petrochemical plants), highly localized and time-varying changes in wireless link quality are quite common. Oddly enough, much of the capabilities needed to deal with such effects are already present at the physical layer (PHY), but remain largely unexploited by higher protocol layers. In fact, little Industrial Internet of Things (IoT) (IIoT) research has considered harnessing the full multi-modal capabilities of modern multi-PHY/multi-band IoT hardware in general. As such, in this vision paper, we: (1) analyze recent advances towards enabling multi-modal IIoT through link- and routing layer operations; and (2) describe challenges and opportunities for future IIoT deployments, based on the design choices that emerged from said analysis. In summary, we identify a combination of a modified/extended Time-Slotted Channel Hopping (TSCH) link layer, using either fixed or variable duration timeslots, together with a Parent-Oriented (PO) Routing Protocol for Low-Power and Lossy Networks (LLNs) (RPL) approach to be the most promising way forward.
PubDate: 2023-03-22
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- Performance Analysis of Hybrid Filtering Technique for Reduction OF PAPR
in Alamouti Coded MIMO-OFDM Systems-
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Abstract: Multiple-Input Multiple-Output (MIMO) technique combined with Orthogonal Frequency Division Multiplexing (OFDM) is the proficient air interface for upcoming generations of high data rate and high-speed wireless communication. Nonetheless, such a system suffers from the drawback of a high peak-to-average power ratio (PAPR) when the symbol phases in the OFDM subcarriers line up in such a fashion that constructive formation of a large signal peak in the time domain occurs. Classical amplitude clipping, one of the simplest techniques, is employed to reduce PAPR. However, in-band distortion caused by clipping affects the high-frequency components of the in-band signal because the signal is clipped directly into the discrete-time domain at the Nyquist sampling rate. This causes the aliasing phenomenon through which the high frequency component of the OFDM signal's spectrum actually inherits the low frequency identity of the sampled version of the spectrum. In this paper, we propose an additional filtering scheme that has been assigned to our clipping algorithm to remove the high frequency components within the feasible regions (i.e. the constellation areas of the modulated symbols), which can efficiently reduce the PAPR of OFDM signals. The performance of the hybrid filtering algorithm is evaluated and compared with other algorithms such as the Partial Transmit Sequence (PTS), Selective mapping, active constellation extension for PAPR reduction by computer simulation in Alamouti Coded MIMO-OFDM systems. The simulation has been carried out using Matlab R2018a. Also, the technique proposed is compared with some recent techniques. The hybrid filtering algorithm is found to offer a better PAPR reduction as compared to the other techniques. It is shown that at a clipping ratio of 1.8, a PAPR reduction of 9.818 dB, equivalent to a percentage difference of 97.97% with respect to the original signal is achieved, when employing QPSK modulation and 12.42% more when using clipping only. The proposed work can find applications in Massive MIMO systems for beyond 5G networks.
PubDate: 2023-03-22
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- An Improved Particle Swarm Optimization Algorithm for UAV Base Station
Placement-
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Abstract: In cellular networks, a set of Base Stations (BSs) might be out of service and failed in the aftermath of natural disasters. One of the promising solutions to fix this situation is to send low altitude drones equipped with a small cellular BS (DBSs) to the target locations. This can provide cellular networks with vital communication links and make available temporary coverage for the users in unexpected circumstances. However, finding the minimum number of DBSs and their optimal locations are highly challenging issues. In this paper, a Mixed-Integer Non-Linear Programming formulation is provided, in which the DBSs’ location and the proper number of DBSs are jointly determined. An improved PSO-based algorithm is proposed to jointly optimize DBSs’ locations and find the minimum number of DBSs. As in the original PSO algorithm, the particles are randomly distributed in the initialization phase and a K-means-based clustering method is employed to generate the positions of the first-generation particles (DBSs). In addition, a custom communication protocol is presented for data exchange between the users’ equipment (UE) and the network controller. The proposed approach is evaluated through four simulation experiments implemented using Mininet-Wifi integrated with CopelliaSim. The acquired results show that the proposed solution based on the integration of PSO and K-means algorithms provides a low packet loss and latency. Moreover, it indicates that most of the users in the considered scenarios are covered by the DBSs.
PubDate: 2023-03-22
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- An Effective Design of Hybrid Spectrum Slicing WDM–PDM in FSO
Communication System Under Different Weather Conditions-
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Abstract: The free space optical communication system addressed as optical wireless communication has recently received huge attention. Communication acts as the possible way to address the link capacity and spectral efficiency issues. Due to the requirement of fundamental features like higher data rate transmission, improved data security and congestion-free transmission through the atmosphere, effective research interest is aroused in Free space Optic communication systems. As the Free space Optic technology exchanges a technological legacy with optical fibre communication, the Free space Optic links can deliver effective bandwidth. Even though it provides effective link reliability, the disastrous effect is faced due to the accordance of atmospheric attenuations like rain, snow, fog and so on. Hence, to promote effective data transmission, a hybridized model called spectrum slicing–wavelength division multiplexing–polarization division multiplexing (SS–WDM–PDM) is projected in this research work. The major objective of the projected work is to maximize the link capacity and enhance spectral efficiency. The attenuation noises like light rain, heavy rain, medium rain and light fog are considered to evaluate the outcomes of a Free space Optic communication system. The outcomes of the SS–WDM–PDM-FSO system are analyzed for numerous building heights and wind speeds for distance and received power. The parameters like an optical signal-to-noise ratio (OSNR), bit error rate (BER) and Q factor are contemplated to evaluate the performance. The simulation tool adopted to analyze the performance is MATLAB.
PubDate: 2023-03-22
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- Extreme Learning Machine Based Identification of Malicious Users for
Secure Cooperative Spectrum Sensing in Cognitive Radio Networks-
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Abstract: Cognitive radio (CR) technology has evolved over the traditional radio to successfully utilize the unused frequency spectrum. In CR the secondary users (SUs) perform cooperative spectrum sensing to access the available frequency band. The opportunistic nature of sensing prevents any interference with primary users (PUs) in the network. However, the presence of security threats like malicious users (MUs) strongly influences the performance. In CR network, MUs act like normal SUs and transmit false information to the fusion center and degrades the performance. To overcome this issue, we proposed an extreme learning machine (ELM) based approach to classify the legitimate SUs with the MUs. In this work, ELM is used as a classifier to separate the legitimate SUs and MUs. Extensive simulation results are presented to highlight the effectiveness of the proposed approach. The proposed approach highlights significant improvement in terms of training time and provides better trade-off compare to the other competitive techniques in the literature.
PubDate: 2023-03-21
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