A Discrete Time Markov Chain Model for High Throughput Bidirectional Fano Decoders

TL;DR

This paper models the bidirectional Fano decoding process using a Discrete Time Markov Chain to optimize throughput and hardware complexity in high-speed decoding systems.

Contribution

It introduces a DTMC model for BFA decoding, linking input data rate, buffer size, and clock speed, aiding in high throughput system design.

Findings

Optimal buffer size minimizes hardware complexity.

Tradeoff identified between number of decoders and buffer size.

Model enables design of high throughput parallel BFA systems.

Abstract

The bidirectional Fano algorithm (BFA) can achieve at least two times decoding throughput compared to the conventional unidirectional Fano algorithm (UFA). In this paper, bidirectional Fano decoding is examined from the queuing theory perspective. A Discrete Time Markov Chain (DTMC) is employed to model the BFA decoder with a finite input buffer. The relationship between the input data rate, the input buffer size and the clock speed of the BFA decoder is established. The DTMC based modelling can be used in designing a high throughput parallel BFA decoding system. It is shown that there is a tradeoff between the number of BFA decoders and the input buffer size, and an optimal input buffer size can be chosen to minimize the hardware complexity for a target decoding throughput in designing a high throughput parallel BFA decoding system.