Begränsningsprogrammering för orkestrering av Cloud-RAN-resurser : Modellering och fullständig enumerering med Constraint Programming SAT solver
Siyan Chen · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2026
The rapid growth of network complexity and the increasing demand for flexible infrastructure present significant challenges for automated resource allocation in industrial network systems. Traditional rule-based methods or single-solution optimizers often struggle to manage the combinatorial explosion of device configurations and lack support for dynamic changes or multiple feasible options. This study aims to model the network equipment combination problem as a constraint satisfaction problem (CSP) and address it using state-of-the-art constraint programming techniques. We concentrate on generating all valid, resource-optimal combinations of potential network nodes while minimizing the utilization of additional shared devices. We implement a CSP model utilizing Google OR-Tools’ constraint programming SAT solver (CP-SAT) solver and propose an incremental graph decomposition strategy that partitions the global problem into subgraphs based on connectivity. Each subproblem is solved independently, with their solutions subsequently merged through an efficient deduplication mechanism. Additionally, a mixed integer programming (MIP) version is implemented for comparative analysis. Experimental results indicate that our CP-SAT approach significantly outperforms the MIP model in terms of scalability and solution diversity, particularly in large-scale network scenarios. The method can efficiently enumerate hundreds of optimal configurations within seconds. This work contributes a practical, scalable, and dynamic CSP-based optimization framework for network configuration, which can be extended to other domains involving complex resource allocation and combinatorial design.