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Author: Ali Soltani Tehrani
Publisher:
ISBN: 9789173857673
Category :
Languages : en
Pages :
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Book Description
Author: Ali Soltani Tehrani
Publisher:
ISBN: 9789173857673
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Fadhel M. Ghannouchi
Publisher: John Wiley & Sons
ISBN: 1119004438
Category : Technology & Engineering
Languages : en
Pages : 272
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Book Description
Covers theoretical and practical aspects related to the behavioral modelling and predistortion of wireless transmitters and power amplifiers. It includes simulation software that enables the users to apply the theory presented in the book. In the first section, the reader is given the general background of nonlinear dynamic systems along with their behavioral modelling from all its aspects. In the second part, a comprehensive compilation of behavioral models formulations and structures is provided including memory polynomial based models, box oriented models such as Hammerstein-based and Wiener-based models, and neural networks-based models. The book will be a valuable resource for design engineers, industrial engineers, applications engineers, postgraduate students, and researchers working on power amplifiers modelling, linearization, and design.
Author: Farouk Mkadem
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
The demands for high data rates and ubiquitous/broadband wireless access necessitate the development of radio systems that deploy wide- and multi-band signals. These signals lead to high spectral utilization with negative trade-offs, such as rapidly varying envelopes and high peak-to-average power ratios (PAPRs). To deploy these types of signals, radio frequency (RF) transmitters face several challenges in power consumption, i.e., efficiency, and sources of distortions, i.e., nonlinearity. These challenges are most apparent in power amplifier (PAs) and degrade the overall performances of RF transmitters. PA efficiency and linearity are characteristics that cannot be satisfied simultaneously. At high input power, PAs exhibits high efficiency; however, a PA in that region is inherently nonlinear. Conversely, at low input power, PAs are fairly linear at the expense of efficiency. With the deployment of wide- and multi-band signals, PAs exhibit strong static nonlinearity and memory effects at high input power. These effects lead to distortions, resulting in degradation of the error vector magnitude (EVM) and spectral regrowth. This regrowth creates adjacent channel interference and violates the emission requirements mandated by regulatory bodies. Digital predistortion (DPD) has been devised to mitigate the PA nonlinearity at high input power. Subsequently, DPD improves the achievable PA linearity versus power efficiency trade-off. DPD incorporates an extra nonlinear function before the PA in order to preprocess the input signal. As a result, the cascaded system (DPD+PA) behaves linearly. DPD+PA linearity requires the DPD function to produce nonlinearities that have equal magnitude and are out phased compared to those generated by the PA. Thus, accurate PA behavioral modeling is essential for the development of DPD and is usually explored before DPD development. In the context of wide- and multi-band signals, PA behavioral models/DPD schemes face several problems that have not been addressed appropriately in the literature. One particular problem is the exponential growth of the number of coefficients with nonlinearity order and memory depth. This growth leads to models' high complexity identification and implementation. Therefore, it restricts DPD performances in trading off linearity and efficiency. Moreover, for multi-band signals, the carrier frequencies' separation can be very large (in the order of hundreds of MHz). Consequently, the conventional single-input single-output DPD is not viable due to the unrealistic sampling rate required to cover a large range of frequencies. Finally, because a very high sampling rate is needed in the PA output observation path, the practicability of the DPD is rendered more complex and sometimes unfeasible when it is deploying wideband signals. This thesis explores new schemes suitable for modeling and linearizing PA outputs when driven with wide- and multi-band signals. First, a new pruning technique is introduced to alleviate the complexity of DPD implementation. This pruning identifies the minimum set of dominant kernels needed in the Volterra series modeling scheme based on Wiener G-Functionals. The pruned Volterra series allows significant improvements of the numerical computation and stability of the DPD scheme. Second, new generalized memory polynomial (GMP) models for multi-band DPD are proposed. These multi-input multi-output GMP models show excellent potential in linearizing different nonlinear multi-band PAs. In addition, they involve cross terms and a reduced number of coefficients, which allows robustness against time delay misalignment between the multi-band signals. Finally, a new method to reduce the conventional high output sampling rate in the PA output observation path is proposed. Detailed analysis assesses the extent to which the output-sampling rate can be reduced through careful selection of the set of kernels used to represent the Volterra series DPD. This analysis results in a 2/5 reduction when the G-Functionals pruning technique is deployed. Extensive analysis and measurement validation are discussed and carefully analyzed to prove the validity of the different proposed solutions and to guarantee their generalizability.
Author: Gernot Hueber
Publisher: John Wiley & Sons
ISBN: 1118102207
Category : Technology & Engineering
Languages : en
Pages : 608
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Book Description
Summarizes cutting-edge physical layer technologies for multi-mode wireless RF transceivers. Includes original contributions from distinguished researchers and professionals. Covers cutting-edge physical layer technologies for multi-mode wireless RF transceivers. Contributors are all leading researchers and professionals in this field.
Author: Abdelgader M. Abdalla
Publisher: John Wiley & Sons
ISBN: 1119491584
Category : Science
Languages : en
Pages : 357
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Book Description
The mobile market has experienced unprecedented growth over the last few decades. Consumer trends have shifted towards mobile internet services supported by 3G and 4G networks worldwide. Inherent to existing networks are problems such as lack of spectrum, high energy consumption, and inter-cell interference. These limitations have led to the emergence of 5G technology. It is clear that any 5G system will integrate optical communications, which is already a mainstay of wide area networks. Using an optical core to route 5G data raises significant questions of how wireless and optical can coexist in synergy to provide smooth, end-to-end communication pathways. Optical and Wireless Convergence for 5G Networks explores new emerging technologies, concepts, and approaches for seamlessly integrating optical-wireless for 5G and beyond. Considering both fronthaul and backhaul perspectives, this timely book provides insights on managing an ecosystem of mixed and multiple access network communications focused on optical-wireless convergence. Topics include Fiber–Wireless (FiWi), Hybrid Fiber-Wireless (HFW), Visible Light Communication (VLC), 5G optical sensing technologies, approaches to real-time IoT applications, Tactile Internet, Fog Computing (FC), Network Functions Virtualization (NFV), Software-Defined Networking (SDN), and many others. This book aims to provide an inclusive survey of 5G optical-wireless requirements, architecture developments, and technological solutions.
Author: Ajith Abraham
Publisher: Springer
ISBN: 3319683241
Category : Technology & Engineering
Languages : en
Pages : 480
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Book Description
This volume of Advances in Intelligent Systems and Computing highlights key scientific achievements and innovations in all areas of automation, informatization, computer science, and artificial intelligence. It gathers papers presented at the IITI 2017, the Second International Conference on Intelligent Information Technologies for Industry, which was held in Varna, Bulgaria on September 14–16, 2017. The conference was jointly co-organized by Technical University of Varna (Bulgaria), Technical University of Sofia (Bulgaria), VSB Technical University of Ostrava (Czech Republic) and Rostov State Transport University (Russia). The IITI 2017 brought together international researchers and industrial practitioners interested in the development and implementation of modern technologies for automation, informatization, computer science, artificial intelligence, transport and power electrical engineering. In addition to advancing both fundamental research and innovative applications, the conference is intended to establish a new dissemination platform and an international network of researchers in these fields.
Author: Karun Rawat
Publisher: Springer Nature
ISBN: 3030388662
Category : Technology & Engineering
Languages : en
Pages : 390
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Book Description
This book focuses on broadband power amplifier design for wireless communication. Nonlinear model embedding is described as a powerful tool for designing broadband continuous Class-J and continuous class F power amplifiers. The authors also discuss various techniques for extending bandwidth of load modulation based power amplifiers, such as Doherty power amplifier and Chireix outphasing amplifiers. The book also covers recent trends on digital as well as analog techniques to enhance bandwidth and linearity in wireless transmitters. Presents latest trends in designing broadband power amplifiers; Covers latest techniques for using nonlinear model embedding in designing power amplifiers based on waveform engineering; Describes the latest techniques for extending bandwidth of load modulation based power amplifiers such as Doherty power amplifier and Chireix outphasing amplifiers; Includes coverage of hybrid analog/digital predistortion as wideband solution for wireless transmitters; Discusses recent trends on on-chip power amplifier design with GaN /GaAs MMICs for high frequency applications.
Author: Xinping Huang
Publisher: Artech House
ISBN: 1608075729
Category : Technology & Engineering
Languages : en
Pages : 231
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Book Description
A wireless communication system employs a radio frequency (RF) wave to transmit information bearing signals. In modern digital communication systems, sophisticated modulation techniques are developed to modulate information onto an RF carrier waveform, so as to transmit more information. This new book presents signal processing techniques for reducing impairments of analog and RF circuits in wireless communications systems. Engineers, researchers, and students will find full coverage of the topic, including vector modulators, power amplifiers, vector demodulators, group delay distortion in analog/RF filters, digital beamforming networks, and dual polarization systems. Several applications are discussed, including both single carrier and multi-carrier scenarios.
Author: Paul J. Draxler
Publisher:
ISBN: 9781303161803
Category :
Languages : en
Pages : 225
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Book Description
High data rates and tight spectral limitations in wireless communication systems require high fidelity waveform transmission with minimal distortion. The waveforms currently being used, or envisioned for the future, coupled with the need for higher efficiency radio frequency (RF) power amplifiers (PA), are driving transmitters to include predistortion and to adopt advanced architectures. PA behavioral models are required for system simulations of these advanced architectures and digital predistortion (DPD) algorithms, to quickly evaluate the component impact on system performance. One advanced technology of particular interest in this work is envelope tracking (ET) where the supply voltage is modulated at the envelope rate, keeping the RF signal operating rail to rail, which changes the performance of the RFPA. This dissertation focuses on accurate estimation of nonlinear behavioral models for PAs: both memoryless and with memory effects. Memory effects are the byproduct of physics-based or circuit-based changes within the amplifier with responses on a baseband time scale (rather than at the carrier frequency). When these memory effects become pronounced, they create a shift in the memoryless model. In order to accurately predict system performance, especially when the system includes a DPD block, it is critical to generate behavioral models for RFPAs which capture the shifts in the memoryless model. The thesis presents a new model, termed the Blackbox augmented behavioral characteristics (ABC) model, which includes the response of the internal states of the circuit. Its extraction is demonstrated from measured data using pulsed or modulated waveforms. The generation of modulated waveforms for instrumentation systems has length restrictions and input sequences are used repetitively, so they should be circular. Techniques that synthesize circular waveforms suitable for measurement systems, directly from long modulated waveforms are presented. An "expected gain" model is developed in this work, and a methodology for extracting waveform specific, expected gain models efficiently from measurements of modulated signals (such as WCDMA) is presented. These expected gain models are then applied to DPD. A technique that leverages the circular stationary measurements, memory mitigation, is presented. The DPD algorithm identifies and compensates systematic distortions in the waveform, generating input signals that achieve optimal outputs, compensating for all deterministic memory of the system and quantifying the measurement limits of the system. A number of systematic measurement impairments are encountered in these experiments. Techniques that compensate for these impairments, including phase drift and time alignment are presented. The thesis also describes behavioral modeling include demonstrating DPD using new techniques that stem from truncating and thresholding the Volterra series. When compared with two other published truncated Volterra series forms on standard Class AB PAs, all three memory models perform equally well. When applied to modeling ETPAs, however, the new thresholded Volterra series model is more efficient, using less than a third of the coefficients to describe the ETPA nonlinear memory effects. In this work, techniques are applied experimentally to RFPAs, from handset PAs to base station PAs, built with a wide variety of materials: HBTs on GaAs, LDMOS devices on Si, and HFET devices on GaN, and GaN HFET devices on Si.
Author: Fa-Long Luo
Publisher: John Wiley & Sons
ISBN: 1119562252
Category : Technology & Engineering
Languages : en
Pages : 490
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Book Description
A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
