Energy-Efficient LDPC Acceleration in Open Radio Access Networks Using DRL and QRF

In the evolving landscape of Open Radio Access Networks (O-RAN), achieving both high energy efficiency and computational acceleration is crucial for optimizing network performance. Low-Density Parity-Check (LDPC) decoding, a computationally intensive process widely used in wireless communication, is commonly accelerated by hardware to meet the stringent demands of real-time processing. However, balancing this acceleration with energy efficiency remains a significant challenge. In this paper, we propose a Deep Reinforcement Learning (DRL)-based smart offloading strategy to achieve low-latency, energy-efficient LDPC acceleration using an FPGA-based hardware accelerator. We introduce a Quantile Regression Forest (QRF)-based method for predicting latency and energy consumption in real time, enabling per-block decision-making, alongside a Proximal Policy Optimization (PPO)-based algorithm for dynamically adapting offloading weights to optimize energy efficiency. The proposed strategy is implemented on the OpenAir-Interface5G (OAI) and FlexRIC platforms to demonstrate its feasibility within an O-RAN testbed. Evaluation results show that the method adapts dynamically to varying network conditions in under 5 seconds, making offloading decisions in less than 5 µs, resulting in up to 20% energy savings and 14% latency reduction.