Human-Intent-Driven Cellular Configuration Generation Using Program Synthesis

Fuliang Li, Chenyang Hei, Jiaxing Shen, Qing Li, Xingwei Wang

Abstract: Cellular networks are vital for emerging applications like the Metaverse, which impose demanding quality and quantity requirements. This necessitates frequent reconfiguration of both new and existing base stations to balance network service quality (e.g., ultra-low latency and high bandwidth) and resource consumption. Existing data-driven configuration methods learn from historical data, but have two key limitations. First, they yield only approximate solutions, lacking precision. Second, poor bootstrapping for new base stations with previously unobserved attributes. In this paper, we pioneer intent-driven configuration synthesis by designing an intent language and utilizing satisfiability modulo theory (SMT) for cellular networks to enable exact and precise solutions. We formulate synthesis as an SMT problem, permitting verification of precision. First, we cast configuration generation as a program synthesis problem via novel modeling to bridge the intent-configuration gap. Second, we extend SMT synthesis to scale to large networks. However, vanilla SMT approaches have poor scalability. Hence, we propose an optimization using sampling for constraint verification instead of exhaustive forward solving. We also design a domain-specific optimization to prune the sample space and improve efficiency. Experiments on various network scales demonstrate the effectiveness of our proposed SMT-based cellular network configuration synthesis.