Modern 5G New Radio (NR) systems demand high data rates, low delays, and consistent reliability, particularly in Cloud-Based Radio Access Network (C-RAN) where standard hardware handles signal processing. To meet these requirements, 5G NR uses Low-Density Parity-Check (LDPC) codes for channel coding. Although LDPC codes provide robust error correction, decoding them is computationally intensive. Consequently, achieving efficient LDPC software decoding is a significant obstacle in creating high-performing and adaptable 5G systems. Previous research has shown that performing Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) decoding in parallel on Graphics Processing Units (GPUs) using proprietary tools like CUDA can yield much higher performance than sequential Central Processing Unit (CPU) approaches. However, relying on such solutions can result in vendor lock-in and reduce portability across hardware platforms. This thesis investigates whether SYCL, a hardware-agnostic parallel programming framework based on C++, can provide efficient QC-LDPC decoding suitable for 5G NR C-RAN environments. This work introduces a QC-LDPC decoder implemented in SYCL that utilizes the Layered Offset Min-Sum algorithm. Its performance is evaluated against a comparable sequential C++ version and a CUDA-based GPU implementation. The experiments employ simulated 5G NR data, varying message sizes, base graph configurations, and channel conditions. The findings indicate that SYCL enables the code to operate on both CPUs and GPUs, though its effectiveness depends on the underlying hardware and specific workloads. On CPUs, SYCL does not surpass the sequential approach due to additional runtime overhead. On GPUs, SYCL shows advantages in the most computationally intensive decoding scenarios but still lags behind native CUDA implementations. These out comes highlight key trade-offs among performance, portability, and reliance on specific vendors when selecting programming models for QC-LDPC decoding in 5G systems.
Anton Nyberg (Thu,) studied this question.