000 04425nam a22005295i 4500
001 978-3-031-01675-2
003 DE-He213
005 20240730164538.0
007 cr nn 008mamaa
008 220601s2010 sz | s |||| 0|eng d
020 _a9783031016752
_9978-3-031-01675-2
024 7 _a10.1007/978-3-031-01675-2
_2doi
050 4 _aT1-995
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
072 7 _aTBC
_2thema
082 0 4 _a620
_223
100 1 _aDongsheng, Bi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985066
245 1 0 _aJoint Source Channel Coding Using Arithmetic Codes
_h[electronic resource] /
_cby Bi Dongsheng, Khalid Sayood, Michael Hoffman.
250 _a1st ed. 2010.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2010.
300 _aVIII, 69 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Communications,
_x1932-1708
505 0 _aIntroduction -- Arithmetic Codes -- Arithmetic Codes with Forbidden Symbols -- Distance Property and Code Construction -- Conclusion.
520 _aBased on the encoding process, arithmetic codes can be viewed as tree codes and current proposals for decoding arithmetic codes with forbidden symbols belong to sequential decoding algorithms and their variants. In this monograph, we propose a new way of looking at arithmetic codes with forbidden symbols. If a limit is imposed on the maximum value of a key parameter in the encoder, this modified arithmetic encoder can also be modeled as a finite state machine and the code generated can be treated as a variable-length trellis code. The number of states used can be reduced and techniques used for decoding convolutional codes, such as the list Viterbi decoding algorithm, can be applied directly on the trellis. The finite state machine interpretation can be easily migrated to Markov source case. We can encode Markov sources without considering the conditional probabilities, while using the list Viterbi decoding algorithm which utilizes the conditional probabilities. We can also use context-based arithmetic coding to exploit the conditional probabilities of the Markov source and apply a finite state machine interpretation to this problem. The finite state machine interpretation also allows us to more systematically understand arithmetic codes with forbidden symbols. It allows us to find the partial distance spectrum of arithmetic codes with forbidden symbols. We also propose arithmetic codes with memories which use high memory but low implementation precision arithmetic codes. The low implementation precision results in a state machine with less complexity. The introduced input memories allow us to switch the probability functions used for arithmetic coding. Combining these two methods give us a huge parameter space of the arithmetic codes with forbidden symbols. Hence we can choose codes with better distance properties while maintaining the encoding efficiency and decoding complexity. A construction and search method is proposed and simulation results show that we can achieve a similar performance as turbo codes when we apply this approach to rate 2/3 arithmetic codes. Table of Contents: Introduction / Arithmetic Codes / Arithmetic Codes with Forbidden Symbols / Distance Property and Code Construction / Conclusion.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_985068
650 0 _aTelecommunication.
_910437
650 1 4 _aTechnology and Engineering.
_985070
650 2 4 _aElectrical and Electronic Engineering.
_985071
650 2 4 _aCommunications Engineering, Networks.
_931570
700 1 _aSayood, Khalid.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985074
700 1 _aHoffman, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_985076
710 2 _aSpringerLink (Online service)
_985080
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031005473
776 0 8 _iPrinted edition:
_z9783031028038
830 0 _aSynthesis Lectures on Communications,
_x1932-1708
_985082
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01675-2
912 _aZDB-2-SXSC
942 _cEBK
999 _c85766
_d85766