Abstract
This paper describes a pipelined iterative technique for joint decoding and channel state estimation of LDPC convolutional codes over Markov channels. Example designs are presented for the Gilbert-Elliott discrete channel model. We also compare the performance and complexity of our algorithm against joint decoding and state estimation of conventional LDPC block codes. Complexity analysis reveals that our pipelined algorithm reduces the number of operations per time step compared to LDPC block codes, at the expense of increased memory and latency. This tradeoff is favorable for low-power applications.
Highlights
LDPC convolutional codes (LDPC-CCs) are the convolutional counterparts of LDPC block codes (LDPC-BCs) and were first presented in 1999 by Feltstrom and Zigangirov [1]
This paper introduces a new algorithm for LDPCCC decoding over Markov channels
We demonstrate that joint decoding and channel state estimation can be performed by adding a few steps to the pipeline decoding algorithm
Summary
LDPC convolutional codes (LDPC-CCs) are the convolutional counterparts of LDPC block codes (LDPC-BCs) and were first presented in 1999 by Feltstrom and Zigangirov [1]. We demonstrate that joint decoding and channel state estimation can be performed by adding a few steps to the pipeline decoding algorithm. We show that when complexity is measured as arithmetic operations per iteration, joint state estimation and decoding is much less complex for LDPC-CCs than for traditional LDPC block codes. Random variables and their quantities are indicated by lower-case Latin letters. Lower-case Greek letters are used to indicate probability messages in the decoding algorithm
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