Modern Coding TheoryCambridge University Press, 17.3.2008 Having trouble deciding which coding scheme to employ, how to design a new scheme, or how to improve an existing system? This summary of the state-of-the-art in iterative coding makes this decision more straightforward. With emphasis on the underlying theory, techniques to analyse and design practical iterative coding systems are presented. Using Gallager's original ensemble of LDPC codes, the basic concepts are extended for several general codes, including the practically important class of turbo codes. The simplicity of the binary erasure channel is exploited to develop analytical techniques and intuition, which are then applied to general channel models. A chapter on factor graphs helps to unify the important topics of information theory, coding and communication theory. Covering the most recent advances, this text is ideal for graduate students in electrical engineering and computer science, and practitioners. Additional resources, including instructor's solutions and figures, available online: www.cambridge.org/9780521852296. |
Sisältö
FACTOR GRAPHS page | 49 |
page Fading Channel | 310 |
8 | 315 |
A ENCODING LOWDENSITY PARITYCHECK CODES page | 437 |
B EFFICIENT IMPLEMENTATION OF DENSITY EVOLUTION | 459 |
CONCENTRATION INEQUALITIES page | 479 |
Channels with Memory Coding for High Spectral Efficiency MultipleAccess Channel | 530 |
Yleiset termit ja lausekkeet
assume asymptotic belief propagation binary binary erasure channel bipartite graph blocklength BMS channel BP decoder check nodes check-node codewords component computation graph Consider converges convolutional codes corresponding defined degree distribution pair denote density evolution discussed elements encoder ensemble LDPC entropy equal equations error probability example EXIT chart EXIT function factor graph Figure fixed point follows FSFG GEXIT Hamming code IEEE IEEE Int IEEE Trans inequality input iterative decoding L-density LDGM LDPC codes LDPC ensembles Lemma linear code low-density parity-check codes MAP decoding messages minimal codewords minimum distance nodes of degree output parameter parity-check matrix performance permutation polynomial Problem Proc proof puncturing random variable residual graph Section sequence shows stability condition strictly positive symmetric Symposium on Inform Tanner graph Theorem Theory threshold turbo codes upper bound Urbanke variable nodes vector weight distribution zero
Viitteet tähän teokseen
Complexity Aspects in Near Capacity MIMO Detection Decoding Ernesto Zimmermann Rajoitettu esikatselu - 2007 |
Bit-Interleaved Coded Modulation Albert Guillén i Fàbregas,Alfonso Martinez,Giuseppe Caire Rajoitettu esikatselu - 2008 |