A Learning-Based Approach to Address Complexity-Reliability Tradeoff in OS Decoders
This work addresses decoding efficiency for communication systems, but it is incremental as it applies neural networks to an existing decoder framework.
The paper tackles the complexity-reliability tradeoff in decoding large linear block codes by using artificial neural networks to predict the required order of an ordered statistics decoder, reducing average complexity and latency, as validated through Monte Carlo simulations.
In this paper, we study the tradeoffs between complexity and reliability for decoding large linear block codes. We show that using artificial neural networks to predict the required order of an ordered statistics based decoder helps in reducing the average complexity and hence the latency of the decoder. We numerically validate the approach through Monte Carlo simulations.