UniDec: A Unified Factor-Graph-Based Decoder Fully Compatible With 5G NR LDPC/Polar Codes

In comparison to 4G, 5G wireless needs to support a broader range of applications. Therefore, both low-density parity-check (LDPC) codes and polar codes have been standardized by 5G new radio (NR) to fulfill the requirements of data channel and control channel, respectively. Usually, LDPC/polar decodings are implemented by separate hardware, leading to low area efficiency. Though decoders which can handle both codes have been proposed, how to compromise between throughput and efficiency has always been a persistent dilemma due to the absence of a unified and smooth integration methodology. To this end, by fully utilizing the common parts of graph-theoretic algorithms for both codes, this paper presents a unified decoder (UniDec) which is fully compatible with 5G NR LDPC/polar codes. This UniDec enables three key approaches: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1) unified processing nodes for both codes</i>, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2) configurable permutation networks with multi-parallelism</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3) flexible scheduling for 5G NR parameter configuration</i>, guaranteeing both high data throughput and area efficiency. Implemented in 40nm CMOS, the UniDec attains a maximum of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$33.64\times $</tex-math> </inline-formula> throughput and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.98\times $</tex-math> </inline-formula> area efficiency compared to its multi-mode counterparts. Even compared with the state-of-the-art (SOA) dedicated ones, the UniDec still maintains a competitive edge in terms of throughput, energy, and area efficiency. It is noted that this methodology can be generalized to other factor-graph based signal processing algorithms.