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Joint Source Channel Coding Using Arithmetic Codes (Paperback)
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Joint Source Channel Coding Using Arithmetic Codes (Paperback)
Series: Synthesis Lectures on Communications
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Based 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
General
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