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Lattice coding for signals and networks : a structured coding approach to quantization, modulation, and multi-user information theory /

"Unifying information theory and digital communication through the language of lattice codes, this book provides a detailed overview for students, researchers and industry practitioners. It covers classical work by leading researchers in the field of lattice codes and complementary work on dith...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Zamir, Ram
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge : Cambridge University Press, 2014.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Lattice coding for signals and networks :  |b a structured coding approach to quantization, modulation, and multi-user information theory /  |c Ram Zamir, Tel Aviv University. 
264 1 |a Cambridge :  |b Cambridge University Press,  |c 2014. 
300 |a 1 online resource 
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520 |a "Unifying information theory and digital communication through the language of lattice codes, this book provides a detailed overview for students, researchers and industry practitioners. It covers classical work by leading researchers in the field of lattice codes and complementary work on dithered quantization and infinite constellations, and then introduces the more recent results on 'algebraic binning' for side-information problems, and linear/lattice codes for networks. It shows how high dimensional lattice codes can close the gap to the optimal information theoretic solution, including the characterisation of error exponents. The solutions presented are based on lattice codes, and are therefore close to practical implementations, with many advanced setups and techniques, such as shaping, entropy-coding, side-information and multi-terminal systems. Moreover, some of the network setups shown demonstrate how lattice codes are potentially more efficient than traditional random-coding solutions, for instance when generalising the framework to Gaussian networks"--  |c Provided by publisher 
520 |a "Unifying information theory and digital communication through the language of lattice codes, this book provides a detailed overview for students, researchers and industry practitioners. It covers classical work by leading researchers in the field of lattice codes and complementary work on dithered quantization and infinite constellations, and then introduces themore recent results on "algebraic binning" for side-information problems, and linear/lattice codes for networks. It shows how high-dimensional lattice codes can close the gap to the optimal information theoretic solution, including the characterization of error exponents"--  |c Provided by publisher 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
505 0 |a Cover; Half title; Title; Copyright; Dedication; Contents; Preface; Acknowledgements; List of notation; 1 Introduction; 1.1 Source and channel coding; 1.2 The information theoretic view; 1.3 Structured codes; 1.4 Preview; 2 Lattices; 2.1 Representation; 2.2 Partition; 2.3 Equivalent cells and coset leaders; 2.4 Transformation and tiling; 2.5 Algebraic constructions; Summary; Problems; Interesting facts about lattices; 3 Figures of merit; 3.1 Sphere packing and covering; 3.2 Quantization: normalized second moment; 3.3 Modulation: volume to noise ratio; Summary; Problems; Historical notes. 
505 8 |a 4 Dithering and estimation4.1 Crypto lemma; 4.2 Generalized dither; 4.3 White dither spectrum; 4.4 Wiener estimation; 4.5 Filtered dithered quantization; Summary; Problems; Historical notes; 5 Entropy-coded quantization; 5.1 The Shannon entropy; 5.2 Quantizer entropy; 5.3 Joint and sequential entropy coding*; 5.4 Entropy-distortion trade-off; 5.5 Redundancy over Shannon; 5.6 Optimum test-channel simulation; 5.7 Comparison with Lloyd's conditions; 5.8 Is random dither really necessary?; 5.9 Universal quantization*; Summary; Problems; Historical notes. 
505 8 |a 6 Infinite constellation for modulation6.1 Rate per unit volume; 6.2 ML decoding and error probability; 6.3 Gap to capacity; 6.4 Non-AWGN and mismatch; 6.5 Non-equiprobable signaling; 6.6 Maximum a posteriori decoding*; Summary; Problems; Historical notes; 7 Asymptotic goodness; 7.1 Sphere bounds; 7.2 Sphere-Gaussian equivalence; 7.3 Good covering and quantization; 7.4 Does packing imply modulation?; 7.5 The Minkowski-Hlawka theorem; 7.6 Good packing; 7.7 Good modulation; 7.8 Non-AWGN; 7.9 Simultaneous goodness; Summary; Problems; Historical notes; 8 Nested lattices. 
505 8 |a 8.1 Definition and properties8.2 Cosets and Voronoi codebooks; 8.3 Nested linear, lattice and trellis codes; 8.4 Dithered codebook; 8.5 Good nested lattices; Summary; Problems; Historical notes; 9 Lattice shaping; 9.1 Voronoi modulation; 9.2 Syndrome dilution scheme; 9.3 The high SNR case; 9.4 Shannon meets Wiener (at medium SNR); 9.5 The mod channel; 9.6 Achieving C[sub(AWGN)] for all SNR; 9.7 Geometric interpretation; 9.8 Noise-matched decoding; 9.9 Is the dither really necessary?; 9.10 Voronoi quantization; Summary; Problems; Historical notes; 10 Side-information problems. 
505 8 |a 10.1 Syndrome coding10.2 Gaussian multi-terminal problems; 10.3 Rate distortion with side information; 10.4 Lattice Wyner-Ziv coding; 10.5 Channels with side information; 10.6 Lattice dirty-paper coding; Summary; Problems; Historical notes; 11 Modulo-lattice modulation; 11.1 Separation versus JSCC; 11.2 Figures of merit for JSCC; 11.3 Joint Wyner-Ziv/dirty-paper coding; 11.4 Bandwidth conversion; Summary; Problems; Historical notes; 12 Gaussian networks; 12.1 The two-help-one problem; 12.2 Dirty multiple-access channel; 12.3 Lattice network coding; 12.4 Interference alignment. 
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650 0 |a Coding theory. 
650 0 |a Signal processing  |x Mathematics. 
650 0 |a Lattice theory. 
650 6 |a Traitement du signal  |x Mathématiques. 
650 6 |a Théorie des treillis. 
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650 7 |a Lattice theory  |2 fast 
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