Wireless Personal Communications最新文献

The purpose of this manuscript is to show that certain acoustic features can be used to recognize the disguised speech of unknown speakers. As the name implies, forensic speaker identification entails the use of scientific techniques to ascertain an unknown speaker’s identity during an inquiry. This study aims to provide a voice recognition method that works well. To distinguish between speech and background noise in each frame, chi-square tests are utilized. The estimated background noise is continuously modified to achieve this. Chi-square noise estimations are then obtained once background noise has initially been reduced. The observed signal distribution and the estimated noise distribution are compared using a second chi-square test, this time using a different approach. For the frame to be labelled as noise, the chi-square test scores must be close together. Mel-frequency cepstrum coefficient (MFCC), features are grouped as three-dimensional features. The correlation coefficient characteristics of speech are coupled with the different MFCC feature extraction technique. The feature-based classification is done with support vector machine (SVM) classifiers and k-nearest neighbor (k-NN) classification technique. Classification results show that applying these unique features in an SVM classifier boosts classification accuracy.

A compact and novel quad-element inset-fed octagonal-shaped multiple input multiple output patch (QIOMP) antenna with a hexagonal-shaped slot is designed for ultra-wideband applications. The proposed MIMO antenna comprises four identical octagonal-shaped radiating elements. To achieve better diversity performance and low mutual coupling, radiating elements are positioned at the ideal distance. The proposed octagonal-shaped MIMO antenna is prototyped on a Fr-4 substrate with the overall dimensions of 32 × 32 × 1.6 mm³. It achieves an ultra-wide bandwidth (S₁₁ < − 10 dB) of 13.9 GHz, operating from 2.5 to 16.7 GHz, except for the band notch from 8.9 to 9.2 GHz. The proposed antenna has a gain of 3.50 dBi, 4.40 dBi, 6.67 dBi, and 5.22 dBi at 4.8 GHz, 8.1 GHz, 10.5 GHz, and 14.1 GHz, respectively. At the operating frequencies, surface current distribution and radiation patterns are examined. The diversity performance of the QIOMP antenna is evaluated by reviewing several MIMO parameters, including an envelope correlation coefficient < 0.03, a diversity gain > 9.8, the total active reflection coefficient, and channel capacity loss < 0.05 bits/sec/Hz. The simulated and experimentally obtained results are presented and exhibit a high degree of concurrence.

This work focus on the classification of skin surface images to identify the psoriatic type. To learn and analysis the deep insight of the psoriatic images a custom Convolutional Neural Network (CNN) developed as a prediction model. Before get into the learning process, the input images are involved with segmentation operation. For this purpose, color and texture feature-based segmentation is utilized. The custom architecture of the CNN is formulated to deliver the superior psoriatic disease type prediction result. The model has experimented with native collected data set and performance measures are analyzed. The results shows that the proposed method has high contribute in terms of psoriasis classification and severity grading with an accuracy of 98.94%.

Noise power (Variance) and signal-to-noise ratio (SNR) are the key performance evaluation parameters of wireless communication systems. In this article, a novel algorithm is proposed that effectively estimates the noise power and SNR for orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems operating over a time-varying Nakagami-m fading channel. The inactive subcarriers in each group of the OFDM-IM system simplify the analysis and lead to noise power estimation under ideal as well as realistic conditions of the wireless channel. At the receiver side, by using the proposed algorithm the position of inactive subcarriers can be identified effectively, as these positions are dynamic in the OFDM-IM system. Another simplified algorithm is proposed at the transmitter side for effectively transmitting the inactive subcarriers. The noise power on the sub-channel, average noise power, and average SNR are calculated. Differential noise power can also be estimated to track the channel variations effectively. None of the noise power and SNR estimators are available in the literature for the OFDM-IM systems. Hence, the performance of the proposed estimator is compared with the noise power and SNR estimators available for the classical OFDM systems. Moreover, the performance is also tested in terms of normalized mean square error (NMSE) using extensive computer simulations. The proposed estimator achieves significant improvements in spectral efficiency, energy efficiency, and also achieves much lower computational complexity in contrast to the estimators for the classical OFDM systems.

One important innovation in information and coding theory is polar code, which delivers capacity attaining error correction performance varying code rates and block lengths. In recent times, polar codes are preferred to offer channel coding in the physical control channels of the 5G (5th Generation) wireless standard by 3GPP (Third Generation Partnership Project) New Radio (NR) group. Being a part of the physical layer, Channel coding plays key role in deciding latency and reliability of a communication system. However, the error correction performance degrades with decreased message lengths. 5G NR requires channel codes with low rates, very low error floors with short message lengths and low latency in coding process. In this work, Distributed Cyclic Redundancy Check Aided polar (DCA-polar) code along with Cyclic Redundancy Check Aided polar (CA-polar) code, the two variant of polar codes have been proposed which provide significant error-correction performance in the regime of short block lengths and enable early termination of decoding processes. While CRC bits improve the performance of SCL (successive cancellation list) decoding by increasing distance properties, distributed CRC bits permit path trimming and early-termination of the decoding process. The design can reduce the decoding latency and energy consumption of hardware, which is crucial for mobile applications like 5G. The work also considers the performance analysis of NR polar codes over AWGN (Additive White Gaussian Noise) for short information block lengths at low code rates in the uplink and downlink control channels using SNR (Signal to Noise Ratio) and FAR (False Alarm Rate) as the performance measures. Simulation results illustrate different trade-offs between error-correction and detection performances comparing proposed NR polar coding schemes.

The exponential growth of technology and advent of the Internet of Things (IoT) paradigm have caused large volumes of data to be continuously generated from the intelligent devices. One common feature of these devices is their limited capabilities, hence, they are not able to process large volumes of generated data. However, the processing of these data in the cloud leads to high latency and high power consumption. Hence, providing services to the latency-sensitive IoT applications in the cloud is a challenging issue. Fog computing as a complement to the cloud, allows data to be processed near IoT devices. However, the resources in the fog layer are heterogeneous. Thus, the proper distribution of tasks among heterogeneous nodes while serving the task within the intended deadline is of great importance. In this paper, we have presented a task scheduling model in the fog-cloud paradigm, which formulates the task scheduling problem as a multi-objective optimization problem with the aim of minimizing service response time and the total energy consumption of the system, while considers deadline and load balancing constraints. Since the problem of task scheduling is np-hard, we have proposed a modified version of Strength Pareto Evolutionary Algorithm II (SPEA-II) with customized operators to achieve the optimal scheduling strategy. The experimental results reveal that the proposed scheme outperforms some benchmarking algorithms in terms of service response time and energy consumption. Furthermore, by optimal distribution of tasks among heterogeneous computing nodes, it leads to better resource utilization and improvement in the percentage of missed-deadline tasks.

In Vehicular Ad Hoc Networks, reliable information transmission relies on an effective routing strategy. Most existing reinforcement learning-based routing methods are ineffective in dynamic network environments and cannot prevent inefficient network routing. Efficient network routing can be controlled by network traffic management, so this paper proposes an intelligent routing strategy based on Deep Reinforcement Learning to enhance routing performance. By integrating intersection forwarding and traffic awareness capabilities, this paper addresses the problem of local optimality and utilizes the Deep Q Network to make intersection forwarding decisions. The state space of this strategy consists of intersection nodes, road information between intersections, and forwarding packet information. When a vehicle node carrying a packet approaches an intersection based on the state space, the intersection node uses a neural network to select the optimal next-hop relay intersection from past learning experiences. It generates appropriate vehicle routing decisions based on information from the current and candidate relay intersections. Finally, we use the real taxi trajectory data of Beijing City to conduct extensive simulation experiments. Simulation results and analysis demonstrate that the proposed strategy outperforms related research regarding higher average packet delivery ratio, shorter average end-to-end delay, and lower average overhead ratio in dense and sparse traffic periods under real road environments. Consequently, this strategy provides efficient and reliable message transmission services for Vehicular Ad Hoc Networks.

Since the outbreak of the novel coronavirus, Covid-19 has continuously spread across the globe briskly. Countries have undertaken different types of measures to blunt this spread varying from lockdowns to curfews to social distancing to compulsory wearing of protective kits, which has been sporadically fruitful. However, despite these stringent measures, which have their own pitfalls, scientists across the globe have been struggling to develop a suitable mathematical model that could depict the existing disease spreading pattern and also predict a trend of numbers in the forthcoming months or years. In this paper, popularly used mathematical models including Polynomial Regression, Auto Regressive Integrated Moving Average (ARIMA) and Deep learning techniques such as Recurrent Neural Network (RNN) have been explored for 5 countries badly affected by this virus. The models were tested from 16th May, 2020 till 22nd May, 2020 and used for predicting future cases and deaths from 23rd May, 2020 to 30th June, 2020. The current research primarily focuses on forecasting the behaviour of total confirmed cases and deaths in each country and further analysing the performance parameters such as Mean Squared Error, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). It has been observed that the polynomial regression model provides a best fit solution at par with actual numbers of confirmed and death cases for India by producing minimum RMSE and MAPE. For South Korea and Italy, the ARIMA and RNN models have shown fidelity with actual numbers. RNN model has shown conformity with US numbers while ARIMA model has found closeness to United Kingdom data. The purpose to perform data analysis is to measure the performance metrics by using different techniques and depict the pattern for each country. Furthermore, the paper also highlights the future predictions for every country to control the spread of disease, save lives, avoid health systems breakdowns and benefit the researchers in this field.

As the world is moving towards the Internet of Things (IoT), more data rate is required with wider coverage and small delay. While the 5G supports the needed increased data rate, it suffers from a decreased coverage per tower. In contrast, the low population areas such as deserts, oceans, forests, and mountains contain most of the world's transportation such as highways, ships, and planes as well as large areas of agricultural lands that are all not supported by 5G. Consequently, LEO satellite constellations have been introduced to cover the earth's surface with a high-speed data rate. Each LEO satellite constellation requires 4000 + satellites and at least one ground base station (BS) for control, tracking, telemetry, and remote sensing functions. However, to support the needed downlink high-speed data rate demand, the LEO satellite constellation must add many BSs to increase the uplink data rate. As a result, the GEO communication satellites face great competitors in their market. This paper introduces the hybrid LEO-GEO satellite communication system to change this competition into cooperation that benefits both systems. In the hybrid LEO-GEO satellite communication system, this paper proposes maximizing the downlink utilization of both LEO and GEO satellites by the traffic-aware Artificial Expectation Detection (AED) technique. In such a technique, the trending multicast data choose the GEO link while the unicast data and control data choose the LEO link to maximize the downlink utilization efficiency. Our results show that using AED is power efficient and delay efficient while increasing the data rate by (100)x to (100k)x or decreasing the needed number of LEO BSs.

The combination of non-orthogonal multiple access (NOMA) and intelligent reflective surface (IRS) is an efficient solution to significantly increase the energy efficiency of the wireless communication system. This paper considers a downlink IRS-based Millimeter wave (mmWave) massive Multiple-Input Multiple-Output (MIMO)-NOMA system. In this work, a two-layer hierarchical AGglomerative NESting- DIvisie ANAlysis Clustering Algorithm (AGNES-DIANA) user grouping (2L-HAD-UG) is proposed to group users. In the proposed 2L-HAD-UG, the first layer will use the AGNES algorithm to efficiently group the users into different clusters. After the first level of clustering, some user groups are larger than others, and perhaps some users with weakly correlated channels are assigned to the same groups. To address these concerns, the large user groups are divided into several smaller groups, and each user whose channels are weakly correlated is isolated as a separate group. In the second layer, the DIANA Hierarchical Clustering is used to divide the larger clusters based on the channel correlation value. After user grouping, a new joint active and passive beam-forming design problem is formulated to maximize the achievable rate of each user in each cluster under the Quality of Service (QoS) requirements of other users, the conditions of Successive Interference Cancellation (SIC) decoding rate, the constraints of IRS reflection components and the transmission power restrictions. The formulated optimization problem is solved by proposing a new Successive Chaotic Group Search Approximation (SCGSA) algorithm. With the proposed massive mmWave MIMO-NOMA system, the spectral efficiency, energy efficiency, and sum rate are achieved to 18.56 bits per second (bps)/Hertz (Hz), 57.56 bps/Hz/Watt (W) and 21.58 bps/Hz, respectively.