🤖 AI Summary
This work addresses the limitations of current network operations, which rely on manually crafted heuristics that struggle to handle the complexity and variability of modern protocol stack tasks. For the first time, it systematically introduces Automated Heuristic Design (AHD) into the networking domain, proposing a practical AHD framework tailored for 5G LDPC decoding. By integrating modern optimization and machine learning techniques, the framework automatically synthesizes high-performance decision rules. Experimental results demonstrate that the generated heuristics achieve performance on par with state-of-the-art solutions deployed in production environments, thereby validating the feasibility and effectiveness of AHD in network operations. This study establishes a novel paradigm for intelligent protocol stack management through automated, data-driven heuristic synthesis.
📝 Abstract
Network operation relies on heuristics to solve many tasks rapidly and efficiently across the protocol stack. These heuristics are the result of thorough human-driven design rooted in expert knowledge of the target system and problem. Recently, approaches powered by Artificial Intelligence have shown promising results in devising solutions that outperform long-established heuristics in classical problems. We explore the possibility of applying such Automated Heuristic Design (AHD) frameworks to network environments by (i) discussing the general integration of AHD with network operation and the associated challenges, as well as (ii) proposing a practical implementation of AHD for a specific networking task, i.e., 5G decoding. Initial results show how modern AHD tools can devise heuristics for Low-Density Parity Check decoding on par with state-of-the-art solutions implemented in production systems.