Wireless Personal Communications最新文献

Polar code (PC) innovation has drawn attention from businesses and academics over the decade, especially in the communication sector. The fifth-generation wireless standard (5G) uses polar codes as a coding technique. Concerning short- to intermediate or long-length codes, the polar decoding fails to repair errors in successive cancellation (SC) decoding sufficiently. Still, by employing successive cancellation list (SCL) decoding, SC decoding can more effectively rectify errors. The main disadvantage of SCL is its increased cost driven by throughput and computational complexity. Building polar codes over an AWGN channel with little computational cost remains an ongoing research issue. Therefore, to address the shortcomings of the SC/SCL decoders, the Simplified successive cancellation (SSC) decoder of polar codes with an improved Gaussian Approximation (GA) technique over an additive white Gaussian noise (AWGN) channel is proposed in this work. Compared to the density evolution technique, the SSC decoder with GA will more easily trace the mean log-likelihood ratio (LLR). The SSC decoder is examined using a GA technique at high and low code rates and lengths. The capacity effects of PCs concerning performance metrics like bit error rate (BER) and block error rate (BLER) are realized in detail at various code lengths. The proposed work is compared with conventional Huawei approximation (HA) and other decoding with better improvement in BER and BLER.

Diabetic Retinopathy (DR) is a burgeoning malady in Asian territories. DR causes 5–7% of the total blindness throughout the region. The main aim of this research is to determine whether a patient is suffering from DR or not by the dint of 2-D color fundus retina scans. In this paper, authors have proposed a GUI-based technique an Ensemble Diabetic Retinopathy Detection (EDRD). This method helps in detecting 2D color fundus scans with an efficient approach for finding DR-affected persons within a few seconds using ensemble techniques of CNN and RLU. The visual geometry group (VGG-19) model and adaptive moment estimation optimizer are used for training and reducing error for the developed technique. A maximum accuracy of 92% was obtained for an 80% training set with a 0.001 learning rate and 25 batch size. The proposed research contribution definitively detects whether the given OCT scan with an efficient approach for finding DR-affected persons within a few seconds.

Due to the dynamic nature and limited resources in wireless networks, attack occurrence is inevitable. These attacks can damage or weaken the transmitted packets and threaten the entire system’s efficiency. As a result, in such a situation, great and sometimes irreparable damage will be done to the business. Thus, security and attack prevention in wireless networks become a necessity and are very important. Essence intrusion detection systems determine whether a user’s performance and behavior under the control or activity of a network traffic load is malicious. Since the characteristics of user behavior and network traffic are diverse and numerous, Selecting some features is necessary to improve the classification accuracy. Therefore, in this idea, a new model for estimating the penetration of wireless network-based networks is proposed based on a combination of feature subset selection based on firewall algorithm and fast neural learning networks. In this paper, the proposed idea will use the training set from the data set collected to test intrusion detection systems called KDD Cup to determine network intrusion detection methods and evaluate the proposed model. The proposed idea, based on the results obtained from the simulation and its performance in various experiments, has shown that it has improved significantly in terms of multiple criteria such as accuracy, F-criterion rate, and efficiency compared to the neural network pattern. In other words, the proposed idea performs better than the neural network method in identifying healthy nodes and new malicious intrusions in the target network. The simulation outputs also indicate that the proposed idea has a better classification rate and F-criteria than the FLN methods based on HSO, ATLBO, GA, and PSO. Vector backup machine, multilayer perceptron network, DBN, and S-NDAE have less time.

This paper presents a study of the impact of different types of waveform jamming schemes against remote detonation communication devices based on Long term evolution (LTE) technology. Two main categories of waveform jamming signals (single-carrier and multi-carriers) were used. Also, two jamming scenarios against LTE-based remote detonation devices (jamming the whole LTE frame and jamming the LTE synchronization channel) were investigated. The key point of evaluating the effectiveness of the presented waveform jamming signals is by measuring their ability to successfully cut off the connection between the transmitter and the LTE-based remote detonation device. This is achieved by verifying that the remote detonation device is unable to successfully receive the transmitted information (Deny of Service (DOS) mode). To this end, emulations of these scenarios are held using two Keysight software-defined radio software programs (SystemVue 2015 and VSA 89600). Finally, a real practical experiment of the impact of different waveform jamming schemes against LTE-based remote detonation devices is emulated to present the optimal waveform jamming scheme against these types of devices.

Target tracking is a crucial application in wireless sensor networks. Current algorithms for target tracking primarily involve node scheduling based on trajectory prediction. However, when the target is lost due to prediction errors, a target recovery mechanism initiates a search operation, potentially activating numerous nodes and leading to increased energy consumption. Furthermore, the recovery process may result in data loss. To address these challenges, we propose a fault-tolerant clustering approach using the Cat Optimization Algorithm to minimize the probability of target loss. To assess the effectiveness of our approach, simulations were conducted in OPNET using the NODIC, DCRRP, BFOABMS, and AFSRP protocols. The results illustrate that our method excels over existing approaches across various metrics. Specifically, compared to the well-known NODIC method, our approach reduces end-to-end delay by 84.93%, media access delay by 15.08%, increases throughput rate by 3.84%, lowers energy consumption by 4.49%, improves signal-to-noise ratio by 9.99%, and enhances delivery rate of data to the sink by 1.02%. Additionally, compared to the widely recognized DCRRP method, our method improves media access delay by 2.90%, throughput rate by 2.02%, reduces energy consumption by 0.30%, enhances signal-to-noise ratio by 7.36%, and improves the delivery rate of data to the sink by 0.41%. Moreover, our proposed method decreases the end-to-end delay by 10.28% compared to DCRRP. Also, the superior performance of the proposed method in terms of end-to-end delay is 1.52%, media access delay by 8.73%, throughput rate by 1.97%, energy consumption by 0.33%, signal-to-noise ratio by 9.25%, and delivery rate of successfully sending data to the sink is 0.76% higher than the well-known AFSRP method.Additionally, compared to the widely recognized BFOABMS method, our method improves media access delay by 9.56% and enhances the delivery rate of data to the sink by 0.70%. However, in our proposed method, the energy consumption criterion has increased by 13.63%, the end-to-end delay criterion by 50.78%, the signal-to-noise ratio decreased by 15.66%, and the throughput ratio decreased by 26.88% compared to BFOABMS.

In the future intelligent transportation system (ITSs), there will be a lot of negotiation work between vehicle and vehicle (V2V) and between vehicle and infrastructure (V2I), so it is very necessary to design efficient and energy-saving offloading strategy. Aiming at the three conflicting optimization objectives of offloading delay, energy consumption and load balancing, an efficient and energy-saving offloading decision scheme in the scenario of Internet of vehicles was proposed in this paper. Firstly, the task segmentation model, offloading delay model, energy consumption model, load balancing model and multi-objective optimization model were constructed. Then, based on the comprehensive consideration of data offloading delay, energy consumption and load balance, a task offloading scheme based on MOEA/D was proposed. Finally, the proposed scheme was compared with NSGA-II-based scheme, NSGA-III-based scheme,PESA-II-based scheme and SPEA-II-based scheme. The simulation results show that a task offloading scheme based on MOEA/D is obviously superior to the above schemes in terms of offloading delay, energy consumption and load balancing, and can provide efficient and energy-saving offloading service.

OFDM is superior technique in wireless sensor networks with low power consumption. Channel estimation modelling for low power wireless access might lead to exclusive access of transmission leading to failure of an augmented path. The proposed work models the channel where in intricate scenario of interference, error in carrier frequency offset the possibility to counter sensor data is being initiated from source sensor. The proposed work incorporates residual network architecture and uses two paths for considering a flow from source to sink. The first main path estimates the channel between source and relay then between relay and penultimate node to sink with the objective of minimizing the carrier frequency offset error. Second skip connection estimate the direct forwarding from source to penultimate node to sink for calculating the residual block characteristics. Thus the simulation work shows the proposed Residual Neural Network based OFDM achieves superiority is balancing every flow and superiority than conventional OFDM technique.

In this article a compact sinusoidal tapered slot Vivaldi linear antenna array is designed for X and Ku band applications is presented. Printed Vivaldi antennas are extensively used for broadband applications because it gives high gain and broad bandwidth. The proposed antenna consists of two circular stubs, one rectangular slot and sinusoidal tapered lines on both sides of the antenna structure. These two tapered lines separated by circular slots and rectangular slot. The bandwidth of the antenna is improved with two circular stubs before the tapering. A rectangular slot is included before the two stubs to reduce the aperture width of the antenna. The feed line is then terminated with sectorial stub to have better coupling to the slot. The profile of the tapering of slot is designed as sinusoidal variation to have good end fire radiation for the entire frequency range. The simulated single antenna has return loss less than− 10 dB in between the frequencies 8.2–20 GHz. A five-element linear array is designed, simulated and prototype model is fabricated. The simulated and experimental results show return loss is less than − 7.5 dB for the frequency range of 8.2–20 GHz. The maximum gain of single element and array is 4.6 dBi and 7 dBi respectively. The Co and cross polarization radiation patterns are measured. The designed Vivaldi antenna array is used in X, KU band, electronic warfare and phased array applications.

A new feature extraction technique using Teager Energy Operator is proposed for the detection of stressed sentiments as Teager Energy-Autocorrelation Envelope. TEO is basically designed for increasing the energies of the stressed speech signal whose energies are reduced during the speeches production process and hence, used in these analysis. A stressed speech emotion recognition system is developed employing TEO-Auto-Env and Spectral feature combination for detecting the emotions. Mel frequency cepstral coefficients, linear prediction cepstral coefficients, and relative spectra—perceptual linear prediction are the spectral properties studied. EMO-DB (German), EMOVO (Italian), IITKGP (Telugu) and EMA (English) databases are used in this analysis. The classification of the emotions is carried out using the k-Nearest Neighborhood classifiers for gender-dependent and speaker-independent cases. The proposed SSER system provided improved precision comparison to the previous ones. The greatest classification precision is obtained using the characteristic combination of TEO-Auto-Env, MFCC and LPCC features with 91.4% (SI), 91.4% (GD-Male) and 93.1%(GD-female) for EMO-DB, 68.5% (SI), 68.5% (GD-Male) and 74.6% (GD-female) for EMOVO, 90.6%(SI), 91% (GD-Male) and 92.3% (GD-female) for EMA, and 95.1% (GD-female) for IITKGP female database.

Intrusion Detection Model (IDM) is an essential device for network defence in current trend. Malicious users analyse the vulnerabilities of IDSs to capture unauthorized access. Furthermore, intrusion detection encompasses numerous numerical attributes and models, resulting in elevated detection errors and triggering false alarms. Hence, optimal computational intelligence shall be incorporated in IDM to achieve high detection rate and less number of false alarms. Considering the same, a new hybrid IDM framework is developed as the combination of Fuzzy Genetic Algorithm with Multi-Objective Particle Swarm Optimization that maximizes the detection accuracy, minimizes the false alarms and takes less computational complexity which will be explained first phase. The existing IDSs are constraint to the information trained incur into false positives based on user continuity for normal activity. The objective of this proposal is to extract optimal classification rules automatically from training data that helps to identify types of attacks correctly including the unknown attack types. For achieving this goal, Multi-Objective Particle Swarm Optimization (MOPSO) is used as classifier to enhance the identification of the rare attack classes within the IDM. The effectiveness of this method lies in its capacity to leverage information within an unfamiliar search space, guiding subsequent searches towards valuable subspaces. It provides better separability of various classes’ i.e. normal behaviour and false alarms. In this FGA-MOPSO model, Principal Component Analysis (PCA) serves as the feature selection technique employed to identify pertinent features within the dataset, thereby enhancing the classifier’s performance and Fuzzy Genetic Algorithm (FGA) is used to create new population for training the classifier with the help of three operations namely selection, crossover and mutation that helps to practice more patterns in training phase and to obtain better understanding of the proposed classifier. The simulation will illustrate that the system is competent to speed-up the training and testing process of intrusions detection is important for network applications.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Arun Kumar] Last name [Ramamoorthy]. Also, kindly confirm the details in the metadata are correct.Checked and Verified for Author 1. In Author 2 name, Given Name was [K.] and last name was[Karuppasamy], But its is just the opposite. Given Name is [Karuppasamy] and Last Name is [K.]. I have edited it.