Multi-operator spectrum sharing using matching game in small cells network

TL;DR

This paper addresses spectrum sharing among multiple operators in small cell networks by combining matching game theory and reinforcement learning to optimize resource allocation and maximize social welfare.

Contribution

It introduces a novel framework that extends matching game theory with externalities and integrates reinforcement learning for power allocation in spectrum sharing.

Findings

Pairwise stable matchings achieve local maxima of social welfare.

Proposed algorithms converge efficiently and produce effective solutions.

Simulation results validate the approach's effectiveness.

Abstract

In this paper, we study a problem where multiple operators (OPs) need to share a common pool of spectrum with each other. Our objective is to maximize the social welfare, defined as the overall weighted sum rate of the OPs. The problem is decomposed into two parts: the first part is to allocate RBs to OPs, which we do so by extending the framework of many-to-one matching game with externalities. The second part is to allocate power of small cell base stations (SBSs) belonging to each OP, which is accomplished using reinforcement learning. Assuming that the SBSs associated with each OPs are spatially distributed according to Poisson point process (PPP), we show that pairwise stable matchings achieve local maximas of the social welfare function. We propose two algorithms to search for the stable matchings. Simulation results show that these algorithms are well behaved in terms of convergence and efficiency of the solutions.