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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Lattice coding for signals and networks/ Ram Zamir.
Reminder of title:	a structured coding approach to quantization, modulation, and multi-user information theory /
remainder title:	Lattice Coding for Signals & Networks
Author:	Zamir, Ram.
Published:	Cambridge :Cambridge University Press, : 2014.,
Description:	xx, 437 p. :ill., digital ; : 24 cm.;
Subject:	Coding theory. -
Online resource:	https://doi.org/10.1017/CBO9781139045520
ISBN:	9781139045520

Lattice coding for signals and networks = a structured coding approach to quantization, modulation, and multi-user information theory /
Zamir, Ram.

Lattice coding for signals and networksa structured coding approach to quantization, modulation, and multi-user information theory /[electronic resource] :Lattice Coding for Signals & NetworksRam Zamir. - Cambridge :Cambridge University Press,2014. - xx, 437 p. :ill., digital ;24 cm.

1. Introduction -- 2. Lattices -- 3. Figures of merit -- 4. Dithering and estimation -- 5. Entropy-coded quantization -- 6. Infinite constellation for modulation -- 7. Asymptotic goodness -- 8. Nested lattices -- 9. Lattice shaping -- 10. Side-information problems -- 11. Modulo-lattice modulation -- 12. Gaussian networks -- 13. Error exponents.

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.

ISBN: 9781139045520Subjects--Topical Terms:

561460
Coding theory.

LC Class. No.: TK5102.92 / .Z357 2014

Dewey Class. No.: 003.54

Lattice coding for signals and networks = a structured coding approach to quantization, modulation, and multi-user information theory /
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505 0 $a 1. Introduction -- 2. Lattices -- 3. Figures of merit -- 4. Dithering and estimation -- 5. Entropy-coded quantization -- 6. Infinite constellation for modulation -- 7. Asymptotic goodness -- 8. Nested lattices -- 9. Lattice shaping -- 10. Side-information problems -- 11. Modulo-lattice modulation -- 12. Gaussian networks -- 13. Error exponents.
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.
650 0 $a Coding theory. $3 561460
650 0 $a Signal processing $x Mathematics. $3 528164
650 0 $a Lattice theory. $3 633630
856 4 0 $u https://doi.org/10.1017/CBO9781139045520