A Study of Neural Polar Decoders for Communication

Abstract--In this paper, we adapt and analyze Neural Polar Decoders (NPDs) for end-to-end communication systems. While prior work demonstrated the effectiveness of NPDs on synthetic channels, this study extends the NPD to real-world communication systems. The NPD was adapted to complete OFDM and single-carrier communication systems. T o satisfy practical system requirements, the NPD is extended to support any code length via rate matching, higher-order modulations, and robustness across diverse channel conditions. The NPD operates directly on channels with memory, exploiting their structure to achieve higher data rates without requiring pilots and a cyclic prefix. Although NPD entails higher computational complexity than the standard 5G polar decoder, its neural network architecture enables an efficient representation of channel statistics, resulting in manageable complexity suitable for practical systems. Experimental results over 5G channels demonstrate that the NPD consistently outperforms the 5G polar decoder in terms of BER, BLER, and throughput. These improvements are particularly significant for low-rate and short-block configurations, which are prevalent in 5G control channels. Furthermore, NPDs applied to single-carrier systems offer performance comparable to OFDM with lower PAPR, enabling effective single-carrier transmission over 5G channels. Polar codes, introduced by Arıkan in 2009 [1], are the first class of codes proven to achieve the capacity of symmetric binary-input discrete memoryless channels (B-DMCs) under low-complexity successive cancellation (SC) decoding. In 5G, polar codes are primarily used for control channels, where high performance is required with a low rate and short code length. Their inclusion in the 5G New Radio (NR) standard for uplink and downlink control information, use cases such as enhanced mobile broadband (eMBB) and broadcast channel (BCH) highlight their practical relevance in modern wireless communication systems.