Gibbs Sampling based Spectrum Sharing for Multi-Operator Small Cell Networks

To tackle the challenge of providing higher data rates within limited spectral resources we consider the case of multiple operators sharing a common pool of radio resources in the downlink. The goal is to maintain a long term fairness of spectrum sharing with a no coordination among small cell base stations. It is assumed that the spectral allocations of the small cells are orthogonal to the macro network layer and thus, only the small cell traffic is modeled. We develop a decentralized control mechanism for base stations using Gibbs sampling based learning techniques. Four algorithms are compared addressing the co-primary multi-operator radio resource sharing under heterogeneous traffic in both centralized and distributed scenarios. The performance of these algorithms is assessed through extensive system-level simulations for two indoor small cell layouts. The main performance metrics are user throughput and fairness between operators. The numerical results demonstrate that the proposed Gibbs sampling based learning algorithm provides considerably high throughput while ensuring fairness between OPs.