Six Times to Spare: Characterizing GPU-Accelerated 5G LDPC Decoding for Edge-RSU Communications

TL;DR

This paper evaluates GPU offloading for LDPC decoding in 5G vehicular edge communications, demonstrating significant throughput improvements and reduced CPU load, thus enhancing real-time processing capabilities at the network edge.

Contribution

It provides a detailed microbenchmark analysis of GPU acceleration effects on LDPC decoding across diverse hardware architectures and workload parameters.

Findings

GPU acceleration increases LDPC throughput substantially.

Offloading shifts compute load from CPU to GPU, easing timing constraints.

Dense decoding workloads become more feasible within edge power and timing limits.

Abstract

Ultra-reliable low-latency vehicular communications (URLLC) require sufficient physical-layer (PHY) compute headroom at the network edge, where roadside units (RSUs) and compact next-generation base stations (gNBs) must meet strict timing constraints while co-hosting higher-layer services. In 5G New Radio (5G NR), low-density parity-check code (LDPC) decoding is a latency-sensitive iterative PHY workload whose cost scales with both workload parallelism and decoder iteration budget, making it a potential bottleneck on general-purpose central processing units (CPUs). This paper presents a reproducible, telemetry-backed microbenchmark derived from the Sionna LDPC5G baseline to characterize the compute headroom obtained through graphics processing unit (GPU) offload on compact heterogeneous edge platforms. We evaluate decoder behavior across multiple processor architectures and a wide range of batch sizes and iteration counts, with emphasis on dense operating regimes relevant to edge provisioning. Results show that GPU acceleration substantially increases LDPC throughput, reduces amortized decode service time, and shifts compute pressure away from the CPU, thereby improving the feasibility of meeting edge-RSU timing budgets under heavy parallel workloads. These findings indicate that GPU offload can provide substantial spare PHY compute margin for compact vehicular edge platforms, making dense decode workloads more practical within realistic edge power and timing constraints.