A Regression-Based Optimal Scaling Factor Scheme for 5G LDPC Codes with MIMO Systems

Low-Density Parity Check (LDPC) codes are powerful forward error correction (FEC) codes which achieve near-channel capacity performances. They have been adopted in several technologies, such as WiFi and 5G NR, and there is active research on optimising both their encoding and decoding algorithms. This paper proposes a low-complexity regression-based optimal scaling factor (OSF) scheme to optimise the Normalized Min-Sum Algorithm (NMSA) performance in 5G NR with different Multiple-Input Multiple-Output (MIMO) configurations. The proposed scheme provides E<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf>/N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> gains in all tested scenarios, with the maximum achievable E<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf>/N<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> gain being 1 dB when using the 8T2R MIMO configuration in the low BER range.