Finite State Markov Modeling of Fading Channels Towards Decoding of LDPC Codes

TL;DR

This paper extends LDPC decoding to Markov noise channels with memory, proposing two strategies that incorporate channel estimation, demonstrating performance gains through simulations.

Contribution

It introduces two novel decoding strategies for LDPC codes over Markov channels with memory, including channel estimation, and evaluates their effectiveness.

Findings

Decoding strategies improve performance over Markov channels

Channel estimation enhances LDPC decoding accuracy

Two-state Markov model balances complexity and performance

Abstract

Here we have proposed two decoding strategies of low-density parity-check (LDPC) codes over Markov noise channels with bit flipping noise. The sum-product algorithm used for decoding LDPC codes over memoryless channels is extended to include channel estimation and how much gain we obtain by doing so is simulated and verified. LDPC codes have been studied for years over memoryless channels and are known to have excellent performance. However, these codes over channels with memory is the topic of current research. Here, channels with memory are characterized by Markov modeling which is a useful busty channel model. With sufficient no. of states, they are able to model sufficient noise characteristics. We have gone for a two-state system as it shows a good compromise between complexity and performance.