Automatic recognition of the digital modulation types using the artificial neural networks

As digital communication technologies continue to grow and evolve, applications for this steady development are also growing. This growth has generated a growing need to look for automated methods for recognizing and classifying the digital modulation type used in the communication system, which has an important effect on many civil and military applications. This paper suggests a recognizing system capable of classifying multiple and different types of digital modulation methods (64QAM, 2PSK, 4PSK, 8PSK, 4ASK, 2FSK, 4FSK, 8FSK). This paper focuses on trying to recognize the type of digital modulation using the artificial neural network (ANN) with its complex algorithm to boost the performance and increase the noise immunity of the system. This system succeeded in recognizing all the digital modulation types under the current study without any prior information. The proposed system used 8 signal features that were used to classify these 8 modulation methods. The system succeeded in achieving a recognition ratio of at least 68% for experimental signals on a signal to noise ratio (SNR = 5dB) and 89.1% for experimental signals at (SNR = 10dB) and 91% for experimental signals at (SNR = 15dB) for a channel with Additive White Gaussian Noise (AWGN)

Improving Communication System for Vehicle-to-Everything Networks by Using 5G Technology

Next-generations of wireless communication systems (5G scheme & beyond) are rapidly evolving in the contemporary life. These schemes could propose vital solutions for many existing challenges in various aspects of our lives, eventually to ensure stable communications. Such challenges are even greater when it comes to address ubiquitous coverage and steady interconnection performance in fast mobile vehicles (i.e., trains or airplanes) where certainly blind spots exist. As an early initiative, the Third Generation Partnership Project (3GPP) has proposed a regulation for Long Term Evolution (LTE)-based Vehicle-to-Everything (V2X) network in order to offer solid solutions for V2X interconnections. V2X term should comprise the following terminologies: vehicle-to-vehicle (V2V), vehicle-to-network (V2N) communications, vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P). Superior V2X communications have a promising potential to improve efficiency, road safety, security, the accessibility of infotainment services (any service of user-interface exists inside a vehicle). In this chapter, the aforementioned topics will be addressed. In addition, the chapter will open the door on investigating the role of wireless cooperative and automatic signal identification schemes in V2X networks, and shedding light on the machine learning techniques (i.e, Support Vector Machines (SVMs), Deep Neural Networks (DNNs)) when they meet with the next-generations of wireless networks

Exploring the potential of dynamic mode decomposition in wireless communication and neuroscience applications

The exponential growth of available experimental, simulation, and historical data from modern systems, including those typically considered divergent (e.g., Neuroscience procedures and wireless networks), has created a persistent need for effective data mining and analysis techniques. Most systems can be characterized as high-dimensional, dynamical, exhibiting rich multiscale phenomena in both space and time. Engineering studies of complex linear and non-linear dynamical systems are especially challenging, as the behavior of the system is often unknown and complex. Studying this problem of interest necessitates discovering and modeling the underlying evolving dynamics. In such cases, a simplified, predictive model of the flow evolution profile must be developed based on observations/measurements collected from the system. Consequently, data-driven algorithms have become an essential tool for modeling and analyzing complex systems characterized by high nonlinearity and dimensionality. The field of data-driven modeling and analysis of complex systems is rapidly advancing. Associated investigations are poised to revolutionize the engineering, biomedical, and physical sciences. By applying modeling techniques, a complex system can be simplified using low-dimensional models with spatial-temporal structures described using system measurements. Such techniques enable complex system modeling without requiring knowledge of dynamic equations governing the system's operation. The primary objective of the work detailed in this dissertation was characterizing, identifying, and predicting the behavior of systems under analysis. In particular, characterization and identification entailed finding patterns embedded in system data; prediction required evaluating system dynamics. The thesis of this work proposes the implementation of dynamic mode decomposition (DMD), which is a fully data-driven technique, to characterize dynamical systems from extracted measurements. DMD employs singular value decomposition (SVD), which reduces high-dimensional measurements collected from a system and computes eigenvalues and eigenvectors of a linear approximated model. In other words, by rather estimating the underlying dynamics within a system, DMD serves as a powerful tool for system characterization without requiring knowledge of the governing dynamical equations. Overall, the work presented herein demonstrates the potential of DMD for analyzing and modeling complex systems in the emerging, synthesized field of wireless communication (i.e., wireless technology identification) and neuroscience (i.e., chemotherapy-induced peripheral neuropathy [CIPN] identification for cancer patients). In the former, a novel technique based on DMD was initially developed for wireless coexistence analysis. The scheme can differentiate various wireless technologies, including GSM and LTE signals in the cellular domain and IEEE802.11n, ac, and ax in the Wi-Fi domain, as well as Bluetooth and Zigbee in the personal wireless domain. By capturing embedded periodic features transmitted within the signal, the proposed DMD-based technique can identify a signal’s time domain signature. With regard to cancer neuroscience, a DMD-based scheme was developed to capture the pattern of plantar pressure variability due to the development of neuropathy resulting from neurotoxic chemotherapy treatment. The developed technique modeled gait pressure variations across multiple steps at three plantar regions, which characterized the development of CIPN in patients with uterine cancer. Obtained results demonstrated that DMD can effectively model various systems and characterize system dynamics. Given the advantages of fast data processing, minimal required data preprocessing, and minimal required signal observation time intervals, DMD has proven to be a powerful tool for system analysis and modeling

New Perspectives on Electric Vehicles

Modern transportation systems have adverse effects on the climate, emitting greenhouse gases and polluting the air. As such, new modes of non-polluting transportation, including electric vehicles and plug-in hybrids, are a major focus of current research and development. This book explores the future of transportation. It is divided into four sections: “Electric Vehicles Infrastructures,” “Architectures of the Electric Vehicles,” “Technologies of the Electric Vehicles,” and “Propulsion Systems.” The chapter authors share their research experience regarding the main barriers in electric vehicle implementation, their thoughts on electric vehicle modelling and control, and network communication challenges