Prandoni P., Vetterli M. Signal Processing for Communications

Издательство EPFL Press/CRC Press, 2008, -388 pp.The present text evolved from course notes developed over a period of a dozen years teaching undergraduates the basics of signal processing for communications. The students had mostly a background in electrical engineering, computer science or mathematics, and were typically in their third year of studies at Ecole Polytechnique Fédérale de Lausanne (EPFL),with an interest in communication systems. Thus, they had been exposed to signals and systems, linear algebra, elements of analysis (e.g. Fourier series) and some complex analysis, all of this being fairly standard in an undergraduate program in engineering sciences.
The notes having reached a certain maturity, including examples, solved problems and exercises, we decided to turn them into an easy-to-use text on signal processing, with a look at communications as an application. But rather than writing one more book on signal processing, of which many good ones already exist, we deployed the following variations, which we think will make the book appealing as an undergraduate text.
Less formal: Both authors came to signal processing by way of an interest in music and think that signal processing is fun, and should be taught to be fun! Thus, choosing between the intricacies of z -transform inversion through contour integration (how many of us have ever done this after having taken a class in signal processing?) or showing the Karplus-Strong algorithm for synthesizing guitar sounds (which also intuitively illustrates issues of stability along the way), you can guess where our choice fell.
While mathematical rigor is not the emphasis, we made sure to be precise, and thus the text is not approximate in its use of mathematics. Remember, we think signal processing to be mathematics applied to a fun topic, and not mathematics for its own sake, nor a set of applications without foundations.
More conceptual: We could have said more abstract, but this sounds scary (and may seem in contradiction with point 1 above, which of course it is not). Thus, the level of mathematical abstraction is probably higher than in several other texts on signal processing, but it allows to think at a higher conceptual level, and also to build foundations for more advanced topics. Therefore we introduce vector spaces, Hilbert spaces, signals as vectors, orthonormal bases, projection theorem, to name a few, which are powerful concepts not usually emphasized in standard texts. Because these are geometrical concepts, they foster understanding without making the text any more complex. Further, this constitutes the foundation of modern signal processing, techniques such as time-frequency analysis, filter banks and wavelets, which makes the present text an easy primer for more advanced signal processing books. Of course, we must admit, for the sake of full transparency, that we have been influenced by our research work, but again, this has been fun too!
More application driven: This is an engineering text, which should help the student solve real problems. Both authors are engineers by training and by trade, and while we love mathematics, we like to see their operational value. That is, does the result make a difference in an engineering application?
Certainly, the masterpiece in this regard is C. Shannon’s 1948 foundational paper on The Mathematical Theory of Communication. It completely revolutionized the way communication systems are designed and built, and, still today, we mostly live in its legacy. Not surprisingly, one of the key results of signal processing is the sampling theorem for bandlimited functions (often attributed to Shannon, since it appears in the above-mentioned paper), the theorem which single-handedly enabled the digital revolution. To a mathematician, this is a simple corollary to Fourier series, and he/she might suggest many other ways to represent such particular functions. However, the strength of the sampling theorem and its variations (e.g. oversampling or quantization) is that it is an operational theorem, robust, and applicable to actual signal acquisition and reconstruction problems. In order to showcase such powerful applications, the last chapter is entirely devoted to developing an end-to-end communication system, namely a modem for communicating digital information (or bits) over an analog channel. This real-world application (which is present in all modern communication devices, from mobile phones to ADSL boxes) nicely brings together many of the concepts and designs studied in the previous chapters.
Being less formal, more abstract and application-driven seems almost like moving simultaneously in several and possibly opposite directions, but we believe we came up with the right balancing act. Ultimately, of course, the readers and students are the judges!What Is Digital Signal Processing?
Discrete-Time Signals
Signals and Hilbert Spaces
Fourier Analysis
Discrete-Time Filters
The Z-Transform
Filter Design
Stochastic Signal Processing
Interpolation and Sampling
A/D and D/A Conversions
Multirate Signal Processing
Design of a Digital Communication System