Analysis and Compensation of Power Amplifier Distortions in Wireless Communication Systems

Wireless communication devices transmit message signals which should possess desirable power levels for quality transmission. Power amplifiers are devices in the wireless transmitters which increase the power of signals to the desired levels, but produce nonlinear distortions due to their saturation property, resulting in degradation of the quality of the transmitted signal. This thesis talks about the analysis and performance of communication systems in presence of power amplifier nonlinear distortions. First, the thesis studies the effects of power amplifier nonlinear distortions on communication signals and proposes a simplified design for identification and compensation of the distortions at the receiver end of a wireless communication system using a two-step pilot signal approach. Step one involves the estimation of the channel state information of the wireless channel and step two estimates the power amplifier parameters. Then, the estimated power amplifier parameters are used for transmitter identification with the help of a testing procedure proposed in this thesis. With the evolution of millimeter wave wireless communication systems today, study and analysis of these systems is the need of the hour. Thus, the second part of this thesis is extended to study the performance of millimeter wave wireless communication systems in presence of power amplifier nonlinear distortions and derives an analytical expression for evaluation of the symbol error probability for this system. The proposed analysis evaluates the performance of millimeter wave systems theoretically without the need of simulations, and is helpful in studying systems in the absence of actual hardware

Roadmap of optical communications

© 2016 IOP Publishing Ltd. Lightwave communications is a necessity for the information age. Optical links provide enormous bandwidth, and the optical fiber is the only medium that can meet the modern society's needs for transporting massive amounts of data over long distances. Applications range from global high-capacity networks, which constitute the backbone of the internet, to the massively parallel interconnects that provide data connectivity inside datacenters and supercomputers. Optical communications is a diverse and rapidly changing field, where experts in photonics, communications, electronics, and signal processing work side by side to meet the ever-increasing demands for higher capacity, lower cost, and lower energy consumption, while adapting the system design to novel services and technologies. Due to the interdisciplinary nature of this rich research field, Journal of Optics has invited 16 researchers, each a world-leading expert in their respective subfields, to contribute a section to this invited review article, summarizing their views on state-of-the-art and future developments in optical communications

Advanced DSP Techniques for High-Capacity and Energy-Efficient Optical Fiber Communications

The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to an ever-increasing network load. Over the past decade, optical fiber communication technology has increased per fiber data rate from 10 Tb/s to exceeding 10 Pb/s. The major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP). DSP has played a critical role in accommodating channel impairments mitigation, enabling advanced modulation formats for spectral efficiency transmission and realizing flexible bandwidth. This book aims to explore novel, advanced DSP techniques to enable multi-Tb/s/channel optical transmission to address pressing bandwidth and power-efficiency demands. It provides state-of-the-art advances and future perspectives of DSP as well

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions