Evolutionary learning of scheduling heuristics for heterogeneous wireless communications networks

Network operators are struggling to cope with exponentially increasing demand. Capacity can be increased by densifying existing Macro Cell deployments with Small Cells. The resulting two-tiered architecture is known as a Heterogeneous Network or 'HetNet'. Significant inter-tier interference in channel sharing HetNets is managed by resource interleaving in the time domain. A key task in this regard is scheduling User Equipment to receive data at Small Cells. Grammar-based Genetic Programming (GBGP) is employed to evolve models that map measurement reports to schedules on a millisecond timescale. Two different fitness functions based on evaluative and instructive feedback are compared. The former expresses an industry standard utility of downlink rates. Instructive feedback is obtained by computing highly optimised schedules offline using a Genetic Algorithm, which then act as target semantics for evolving models. This paper also compares two schemes for mapping the GBGP parse trees to Boolean schedules. Simulations show that the proposed system outperforms a state of the art benchmark and is within 17% of the estimated theoretical optimum. The impressive performance of GBGP illustrates an opportunity for the further use of evolutionary techniques in software-defined wireless communications networks.