Название: Recipes for Communication and Signal Processing Автор: Yasir Ahmed Издательство: Springer Серия: Signals and Communication Technology Год: 2023 Страниц: 205 Язык: английский Формат: pdf (true), epub Размер: 42.8 MB
This textbook covers fundamental topics in Telecommunication including Channel Modeling, Modulation/Demodulation, Channel Coding/Decoding, Multicarrier, Capacity, Antenna Arrays, Diversity, and 4G/5G. It will also cover advanced topics such as High-Resolution Spectral Estimation, Reconfigurable Intelligent Surfaces, Index Modulation, Full-Duplex, and Millimeter Wave. This book will mainly target engineering students (both graduate and advanced undergraduate) who are new to the fields of Communication and Signal Processing and are struggling to understand the fundamental concepts. This book will help the students step by step by introducing the concepts first in their most basic form and then providing the code that the students can experiment with. It contains pedagogical elements such as chapter introductions, end-of-chapter questions and numerical problems, MATLAB/Octave/Python code, figures and tables, and a website (raymaps.com) for feedback and interaction. This book provides MATLAB and Python codes for beginners, which help in understanding and visualizing the working of different components of wireless systems. This book covers some novel topics such as full-duplex, reconfigurable intelligent surfaces, and index modulation. It will not only be helpful for undergraduate and graduate students but also for professional engineers and hobbyists.
The capacity of wireless channels has always been a topic of great interest for the researchers, and the discussion of capacity is incomplete without considering the Shannon Capacity. The author of the book explains the Shannon Capacity of different technologies, i.e., GSM, CDMA, LTE and 5G. In practical communication systems, often arrays of antennas are used instead of a single monopole or dipole antenna. Fundamentals of different types of antenna arrays such as linear, rectangular, and circular types are explained in this book with handy MATLAB code. This book also addresses the classical problem of finding the phase and frequency of a signal embedded in noise. In the last part of the book, the author has brought attention to some of the advanced topics of the modern era such as millimeter waves and concerns about health risks of 5G.
Most of the engineers of my generation have grown up using MATLAB for their design and simulation tasks. We have become so comfortable with it that we now even think in terms of vectors and matrices. We apply an averaging filter all the time in our daily life, taking out the inherent randomness. But there are alternatives now, like an open source version of MATLAB, by the name of Octave. Then there is Python which also is also free and open source with tons of libraries and developer resources. So let’s take a deeper dive to discuss the pros and cons of each.
Let us start with the advantages of Python over MATLAB. First of all Python is free and open-source, whereas MATLAB is quite expensive, especially if you are a large company which requires hundreds of concurrent licenses. Secondly, it has a large developer community which is continuously contributing to the ever increasing number of libraries. While MATLAB has only one Integrated Development Environment (IDE), Python users can choose their IDE from very simple ones to more advanced ones with a lot of features. Python is, just like MATLAB, a cross-platform language which can run on all Operating Systems—even embedded systems having a small Linux kernel. All major AI/ML frameworks are based on Python such as Tensorflow, Keras, PyTorch. Lastly, the strength of Python can be gauged from the fact that the image processing for M-87 Black Hole was done using Python.
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