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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
There have been considerable developments in information and communication technology. This has led to an increase in the number of applications available, as well as an increase in their variability. As such, it has become important to understand and master problems related to establishing radio links, the layout and flow of source data, the power available from antennas, the selectivity and sensitivity of receivers, etc. This book discusses digital modulations, their extensions and environment, as well as a few basic mathematical tools. An understanding of degree level mathematics or its equivalent is a prerequisite to reading this book. Digital Communication Techniques is aimed at licensed professionals, engineers, Master s students and researchers whose field is in related areas such as hardware, phase-locked loops, voltage-controlled oscillators or phase noise.
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Table of Contents
Cover
Preface
Introduction
I.1. Why digitize the world?
I.2. Temporal representation of a channel
I.3. The need for coding
I.4. Synoptic bases on information theory
I.5. Codes in linear blocks
I.6. Coding techniques
History Pages
List of Acronyms
1 Modulation
1.1. Modulation?
1.2. Main technical constraints
1.3. Transmission of information (analog or digital)
1.4. Probabilities of error
1.5. Vocabulary of digital modulation
1.6. Principles of digital modulations
1.7. Multiplexing
1.8. Main formats for digital modulations
1.9. Error vector module and phase noise
1.10. Gaussian noise (AWGN)
1.11. QAM modulation in an AWGN channel
1.12. Frequency-shift keying
1.13. Minimum-shift keying
1.14. Amplitude-shift keying
1.15. Quadrature amplitude modulation
1.16. Digital communications transmitters
1.17. Applications
2 Some Developments in Modulation Techniques
2.1. Orthogonal frequency division multiplexing
2.2. A note on orthogonality
2.3. Global System for Mobile Communications
2.4. MIMO
3 Signal Processing: Sampling
3.1. Z-transforms
3.2. Basics of signal processing
3.3. Real discretezation processing
3.4. Coding techniques (summary)
4 A Little on Associated Hardware
4.1. Voltage-controlled oscillator
4.2. Impulse sensitivity function
4.3. Phase noise
4.4. Phase-locked loop
Conclusion
APPENDICES
Appendix 1: Other Examples of Modulation
A1.1. Creating an angular modulation and examples of its application
A1.2. Example of frequency demodulation
Appendix 2: Synopsis on Analog and Digital Modulations
A2.1. AM power frequency spectrum
A2.2. Diode versus coherence
A2.3. Single sideband
A2.4. Variants
A2.5. Summaries
Appendix 3: Fourier Analysis
A3.1 Introduction
A3.2. Eulerian form of the Fourier series
A3.3. Fourier series (Maple/INSA_Lyon/FIMI_2A)
A3.4. Plot of ab and bn according to n
A3.5. Plot of sn and alphan according to n
A3.6. Graphical representation of the signal reconstitution from the Fourier series
A3.7. Manual definition of Fourier coefficients (amplitude and phase)
A3.8. FFT with Matlab
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1. Modulation formats and applications
Table 1.2. Some modulation formats and their spectral eficiency
Table 1.3. Gain obtained on the spectral efficiency and on the binary flow for d...
Table 1.4. Cellular systems
Chapter 2
Table 2.1 A GSM
Chapter 3
Table 3.1 Hamming code
Appendix 2
Table A2.1. Criteria on the digital modulations
Guide
Cover
Table of Contents
Begin Reading
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Series Editor
Guy Pujolle
Digital Communication Techniques
Christian Gontrand
First published 2020 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd
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www.iste.co.uk
John Wiley & Sons, Inc.
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Hoboken, NJ 07030
USA
www.wiley.com
© ISTE Ltd 2020
The rights of Christian Gontrand to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.
Library of Congress Control Number: 2019953804
British Library Cataloguing-in-Publication Data
A CIP record for this book is available from the British Library
ISBN 978-1-78630-540-4
Acknowledgements
Acknowledgements are owed to the non-exhaustive list below:
Chafia Yahiaoui from the Ecole Supérieure d’Informatique d’Alger (Technical University of Algeria), and my telecom colleagues at INSA Lyon: Guillaume Villemaud, Jean-Marie Gorce, Hugues Benoit-Cattin, Attila Baskurt, Stéphane Frenot, Thomas Grenier, Jacques Verdier, Gérard Couturier, Patrice Kadionic, Alexandre Boyer and Carlos Belaustegui Goitia among others, for their detailed observations, as well as their helpful commentaries. Kind acknowledgements also go to Omar Gaouar, my kindly mate at INSA FES, a networker, but also a music buff.
This work is supported by the UpM (Union pour la Méditerranée – Mediterranean Union). It has been accomplished at the Centre d’Intégration en Télécommunication et Intelligence Artificielle (Center of integration in telecommunications and artificial intelligence), INSA FES, UEMF.
Preface
Impressive developments in Information and Communications Technologies (ICT) have naturally led universities and technical schools to develop the electrical engineering (EI) training they provide. This is particularly true in the wireless communications sector. In fact, communications as part of the transmission of data, whether verbal or in video form, is finding more and ever more varied applications. It is becoming necessary for future graduates to understand and master problems linked to the implementation of radio links, depending on the environment, formatting and source data flow, on the power available to the antenna and on the receiver’s selectivity and sensitivity.
This book only requires an introductory level of understanding in mathematics. It does not aim to suffice in and of itself, but rather to convince the reader of the wealth of this domain and its future, to provide good building blocks that will lead to fruition elsewhere. Manufacturers’ concise application notes also seem vital for any researcher/engineer.
Technological innovation plays a very important role in the ICT domain. It therefore seems necessary for training courses now to provide well-adapted and innovative content in teaching and associated tools, while still mastering, as well as possible, the fundamental nature of teaching, which is the only guarantee of a solid and lasting education.
This book is aimed at professional diploma students and engineering and masters students. However, it could also perhaps be aimed at researchers in related domains, such as that of hardware, with, for example, phase-locked loops and their central components: voltage-controlled oscillators, and the famous associated phase noise. Of course, there is an entire domain linked to what is known as firmware, which must be taught, but there are also mathematical tools already in use, for relativity for example, or cryptography, indeed, older forms of coding must be revisited, such as that of Claude Shannon.
Christian GONTRAND
November 2019
Introduction
The word “communication” is now a catch-all in modern society; in its most basic sense, it makes it possible to share information. A department that in any French university or technical school might historically have been labeled as “humanities” (at the end of the 1960s, particularly focused on human resources or sociology); was often later reduced to “communication and humanities”, both terms having become interchangeable in the meantime. Perhaps, now devoid of a clear meaning, nothing will be left apart from the term communication?
This word must not be amalgamated into others: information (transport), (en)coding. Perhaps later semantics are involved in this book, in a strict, technical sense, certainly not in any modernistic sense.
I.1. Why digitize the world?
For broadband communications, transmissions are limited by physical constraints, such as noise or interference, resulting from system imperfections and physical components modifying the transmission of the signal sent. Distortion of the signal over the course of the broadcast is, similarly, a concern. Hence, there is a need for a clear separation of the signals sent, so that, in practice, they remain distinct when they are received.
The transmission of a set of signals undergoes data dispersion over time, leading to intersymbol interference. Signals reflected from buildings, the ground or vehicles cause this dispersion, depending on the length of the paths traveled. The significance of this phenomenon depends on the frequency (above all high frequency), which can vary stochastically, via, for example, the signal’s phases over time (after reflection of obstacles: echoes). They often generate signals, added destructively, or at reception. The resulting signal will therefore be very weak, or sometimes almost nonexistent. These signals can also be added constructively; the final signal will therefore be more powerful than one that arrives via a direct path. We note that multiple paths do not present only drawbacks, since they enable communication even when the transmitter and receiver are not in direct contact (for example, Transcontinental Communications).
A signal is often corrupted when it crosses different paths between transmitter and receiver: data bits that reach the receiver are subject to delays. This distorted signal will be interpreted poorly by the receiver.
In broadband communications, signals are limited by constraints: transmission errors are attenuated when the signal is digitized. For example, for the voice, the amplitude of the signal is typically measured 8,000 times per second and its value is coded in an 8-bit sequence (of 0s and 1s) – we refer here to sampling. The receiver decodes the sequence of the original signal, thus reconstructing the signal sent. Using only 0s and 1s leads to a low (or indeed non-existent) probability of error. The propagation channel can be modeled via an impulse response (see: linear system, Dirac comb); the signal received r(t) is therefore none other than the filtering of the signal sent x (T) through the propagation channel c (t) and can therefore be written in baseband, via a convolution to which noise is often added (see Langevin term added), modeling the system imperfections. Reference is made to frequency-selective channels when the signal transmitted x (t) occupies a [–W / 2, W / 2] frequency band, which is wider than the propagation channel’s coherence bandwidth, c (t), (propagation channel defined as the inverse of the propagation channel’s maximum delay spread Tr).
In this case, the frequential components of x(t) separated from the coherence bandwidth undergo different attenuations. In broadband digital systems, symbols are often sent at a regular interval of time T, at a maximum path delay time Tr; the signal received at an instant t can be expressed as a weighted sum (affected by path attenuations) of the signal transmitted simultaneously (the propagation time for the electromagnetic waves is often neglected, as these propagate at the speed of light) and signals sent at previous instants, a multiple of the (sampling) period.
I.2. Temporal representation of a channel
The coefficients of the propagation channel are given by the values taken for various multiple moments of T: [|c(0)|, |c(T)|, |c(2T)|,|c(3T)|, |c(4T)|, |c(5T)|]. If we focus on mobile radio, between buildings, at 5Ghz, T is in the order of 50 ns; Tr equates to 450 ns.
Designers need to reduce interference caused by multiple reflections of the signal and extract the signal. Equalization means balancing the effects of distortions resulting from these multiple paths. To do this, it is necessary to identify the attenuation coefficients that model the effect of the propagation channel c (t).
Current technologies, used in industrial applications, call on training sequences; a “chosen sequence” is sent regularly, known by the sender and the intended recipient. This method makes it possible to know the channels’ different phase shifts and delays, and gives good results in practice. On the other hand, if the sampling period is too short in relation to the delay Tr (as is the case with high flow transfers; the number of coefficients c(iT) (typically: 0 ≤ i ≤ 5) to be determined can be great, see matrix inversion). Thus, the transmission of high flows when there are several paths present can quickly increase the complexity and therefore the cost of the terminals.
A channel’s selective frequency: the signal to be transmitted has frequency components attenuated differently through the propagation channel. This phenomenon is produced when the signal has a broader frequency band than the propagation channel’s consistent band. A channel’s consistent band is defined as the minimum pass band for which losses from the two channels are independent. This phenomenon is one of the main obstacles to transmission reliability: in fact, it is necessary to estimate the channel (which triggers a loss of flow in moving environments) and also to equalize it (which increases receiver complexity).
Digital equalizer complexity depends on the number of the propagation channel’s paths (determined by the relationship between the duration of equalization, Tr, and the sampling period, T), but also the type of constellation transmitted – see Fresnel diagram. The bits are transmitted in the form of symbols rather than as they are. The number of bits contained in each symbol indicates the size of the constellation; the greater this size, the higher the flow. The average size of these constellations generally has a fixed threshold because of the power limits at the terminals.
Why is not it possible to increase the flow indefinitely by increasing constellation size? The transmission rate can be increased by enlarging the constellation. But, if we speak of the rate as the number of bits per second arriving perfectly at the receiver, then this is impossible; the greater the size of the constellation (at a fixed power, which is always normalized for questions of transmission cost), then the closer the values of the symbols transmitted. It is not easy therefore for the receiver to discriminate between two values riddled with errors resulting from noise. We can really increase the flow (i.e. transmission speed) by increasing the constellation. The rate therefore has a threshold called channel capacity. The idea of an error-free transmission was scarcely imagined by scientists at the end of the 1950s. At this time, it was natural to reduce the probability of transmission errors by reducing binary flow, thus defining channel capacity. It was only with the work of Claude Shannon at the start of the 1920s that encoding emerged to solve this dilemma.
I.3. The need for coding
Figure I.1.Different types of codes
