Advances in Polar Code Decoding for Next-Generation Communication Systems: A Comprehensive Review
TL;DR
The paper proposes the recent advances made in decoding algorithms and hardware implementations, with particular emphasis on recent improvements to Successive Cancellation List (SCL) decoders, which work to improve decoding speed and latency while reducing power area constraints of the decoders, thus making polar codes ever closer to real-time deployments in contemporary communication systems.
Abstract
Polar codes have become a milestone in forward error correction, as they can achieve the channel capacity for Binary-input Memoryless Symmetric (BIMoS) channels and Binary input Partial Response (BPR) channels. Nowadays, their practical deployment still suffers challenges, particularly at short to moderate block lengths, because of the high computational complexity and limited level of parallelization offered by polar codes. The paper proposes the recent advances made in decoding algorithms and hardware implementations, which particular emphasis on recent improvements to Successive Cancellation List (SCL) decoders. The developments can be categorized into three: algorithmic enhancements (Fast-SSCL, polarization-adjusted convolutional codes), parallelization schemes (g-computation, g-branching), and hardware accelerations (unrolled architectures, approximate maximum-likelihood decoding). Together, they work to improve decoding speed and latency while reducing power area constraints of the decoders, thus making polar codes ever closer to real-time deployments in contemporary communication systems. Integrating these methodologies with polar codes may significantly enhance communication. The comparative study of the literature survey demonstrates the high performance of decoder, which can reduce implementation complexity. Existing studies largely focus on terrestrial channels, with insufficient domain-specific evaluation of advanced FEC schemes under GNSS and optical satellite channel impairments. This is one of the research gaps identified.