Performance Analysis and Optimization Based on Adaptive Modulation and Chaotic Interleaving for Helicopter-Satellite Communications

Overcoming periodic blockage of the received signal and the carrier frequency fluctuations caused by rotor blades and Doppler shift, respectively, are considered the most challenging issues in helicopter-satellite communication systems. In this study, we propose an automatic frequency control method based on an adaptive modulation scheme. We employ a hybrid modulation not only depending on quadrature phase shift keying, but also based on binary phase shift keying for accurate cancellation of periodic blockage. On the other hand, we apply a chaotic interleaving scheme with a hybrid modulation scheme in order to achieve a better Bit Error Rate (BER) performance in LOS and NLOS environments. Finally, we validate the mathematical analysis of the proposed scheme through simulations. We evaluated the performance of the proposed scheme and performed a comparison with conventional schemes. Our results show that the proposed scheme is significantly capable of reducing the acquisition time and working with various velocities of helicopter blades in addition to providing a better BER performance in shadow fading environments.     

Resource and power allocation for achieving rate fairness in D2D communications overlaying cellular networks

Wireless Networks - This paper presents a novel resource and power allocation scheme for device-to-device (D2D) communications overlaying cellular networks. The proposed scheme is implemented in...     

Automatic PV Grid Fault Detection System with IoT and LabVIEW as Data Logger

Fault detection of the photovoltaic (PV) grid is necessary to detect serious output power reduction to avoid PV modules’ damage. To identify the fault of the PV arrays, there is a necessity to implement an automatic system. In this IoT and LabVIEW-based automatic fault detection of 3 × 3 solar array, a PV system is proposed to control and monitor Internet connectivity remotely. Hardware component to automatically reconfigure the solar PV array from the series-parallel (SP) to the complete cross-linked array underneath partial shading conditions (PSC) is centered on the Atmega328 system to achieve maximum power. In the LabVIEW environment, an automated monitoring system is developed. The automatic monitoring system assesses the voltage drop losses present in the DC side of the PV generator and generates a decimal weighted value depending on the defective solar panels and transmits this value to the remote station through an RF modem, and provides an indicator of the faulty solar panel over the built-in Interface LabVIEW. The managing of this GUI indicator helps the monitoring system to generate a panel alert for damaged panels in the PV system. Node MCU in the receiver section enables transmission of the fault status of PV arrays via Internet connectivity. The IoT-based Blynk app is employed for visualizing the fault status of the 3 × 3 PV array. The dashboard of Blynk visualizes every array with the status.     

Sum Rate Maximization-based Fair Power Allocation in Downlink NOMA Networks

Non-orthogonal multiple access (NOMA) has been seen as a promising technology for 5G communication. The performance optimization of NOMA systems depends on both power allocation (PA) and user pairing (UP). Most existing researches provide sub-optimal solutions with high computational complexity for PA problem and mainly focuses on maximizing the sum rate (capacity) without considering the fairness performance. Also, the joint optimization of PA and UP needs an exhaustive search. The main contribution of this paper is the proposing of a novel capacity maximization-based fair power allocation (CMFPA) with low-complexity in downlink NOMA. Extensive investigation and analysis of the joint impact of signal to noise ratio (SNR) per subcarrier and the channel gains of the paired users on the performance of NOMA in terms of the capacity and the user fairness is presented. Next, a closed-form equation for the power allocation coefficient of CMFPA as a function of SNR, and the channel gains of the paired users is provided. In addition, to jointly optimize UP and PA in NOMA systems an efficient low-complexity UP (ELCUP) method is proposed to be incorporated with the proposed CMFPA to compromise the proposed joint resource allocation (JRA). Simulation results demonstrate that the proposed CMFPA can improve the capacity and fairness performance of existing UP methods, such as conventional UP, and random UP methods. Furthermore, the simulation results show that the proposed JRA significantly outperforms the existing schemes and gives a near-optimal performance.     

Spectrum Allocation for Enhanced Cross-Tier Interference Mitigation with Throughput Improvement for Femtocells in a Heterogeneous LTE Cellular Network

Wireless Personal Communications - Femtocells are the solution to improve cellular system capacity in indoor coverage. In two-tier networks, co-channel interference is a serious problem. In this...     

Efficient user pairing algorithm for enhancement of spectral efficiency and interference cancelation in downlink NOMA system

A new subcarrier-user allocation algorithm for the downlink non-orthogonal multiple access system is presented in this paper. The proposed algorithm aims to enhance the spectral efficiency of the system and the successive interference cancelation performance by guaranteeing a high difference in channel-gain between the paired users per subcarrier. To enhance the spectral efficiency, the proposed algorithm provides a higher priority to the subcarrier that has a higher best (maximum) channel gain value rather than that has a lower best channel gain value. Also, it pairs the strong user with the second minimum channel-gain user rather than the minimum channel gain user. Besides, the proposed algorithm divides the subcarriers into two groups according to the standard deviation of the channel gain of each subcarrier. Then, it gives the priority to the group with low standard deviation values during subcarrier-user allocation to guarantee a high difference in channel-gain between the paired users per subcarrier. Later, fractional transmit power allocation is applied to distribute the subcarrier power between the paired users. Simulation results prove that the proposed algorithm improves the spectral efficiency of the system, and guarantees a significantly higher difference in channel-gain between the paired users per subcarrier compared to the conventional algorithms.

     

Throughput maximization for multimedia communication with cooperative cognitive radio using adaptively controlled sensing time

Multimedia Tools and Applications - In the last years, most researches proved that spectrum holes are not efficiently utilized in wireless communications. Cognitive radio (CR) is an efficient...     

Efficient remote access system based on decoded and decompressed speech signals

Multimedia Tools and Applications - This paper investigates the effect of both decoding and decompression on the Speaker Identification (SI) in a remote access system. The coding and compression...     

Efficient Deep CNN Model for COVID-19 Classification

Coronavirus (COVID-19) infection was initially acknowledged as a global pandemic in Wuhan in China. World Health Organization (WHO) stated that the COVID-19 is an epidemic that causes a 3.4% death rate. Chest X-Ray (CXR) and Computerized Tomography (CT) screening of infected persons are essential in diagnosis applications. There are numerous ways to identify positive COVID-19 cases. One of the fundamental ways is radiology imaging through CXR, or CT images. The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality. Hence, automated classification techniques are required to facilitate the diagnosis process. Deep Learning (DL) is an effective tool that can be utilized for detection and classification this type of medical images. The deep Convolutional Neural Networks (CNNs) can learn and extract essential features from different medical image datasets. In this paper, a CNN architecture for automated COVID-19 detection from CXR and CT images is offered. Three activation functions as well as three optimizers are tested and compared for this task. The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it. The performance is tested and investigated on the CT and CXR datasets. Three activation functions: Tanh, Sigmoid, and ReLU are compared using a constant learning rate and different batch sizes. Different optimizers are studied with different batch sizes and a constant learning rate. Finally, a comparison between different combinations of activation functions and optimizers is presented, and the optimal configuration is determined. Hence, the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage. The proposed model achieves a classification accuracy of 91.67% on CXR image dataset, and a classification accuracy of 100% on CT dataset with training times of 58 min and 46 min on CXR and CT datasets, respectively. The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10⁻⁵ and a minibatch size of 16.     

Fairness and throughput enhancement based resource allocation scheme for underlay cognitive radio networks

Acquiring good throughput and diminishing interference to primary users (PU) are the main objectives for secondary users in a cognitive radio (CR) network. This paper proposes a centralized subcarrier and power allocation scheme for underlay multi-user orthogonal frequency division multiplexing considering the rate loss and the interference those the PU can tolerate. The main purpose of the proposed scheme is to efficiently distribute the available subcarriers among cognitive users to enhance both the fairness and the throughput performance of the cognitive network while maintaining the QoS of primary users. Simulation results show that the proposed scheme achieves a significantly higher CR network throughput than that of the conventional interference power constraint (IPC) based schemes and provides a significantly enhanced fairness performance. Also, contrary to the conventional IPC based schemes, the proposed scheme is able to significantly increase the achieved throughput as the number of CR users increases.