Distributed Channel Access for Device-to-Device Communications: A Hypergraph-Based Learning Solution

Abstract

In this letter, we propose a learning solution for distributed channel access in device-to-device communications based on a hypergraph interference model. We first define a new interference metric for a hypergraph model, and then formulate this distributed channel access problem as a local altruistic game, which is proved to be an exact potential game admitting at least one pure strategy Nash equilibrium (PNE). A distributed learning algorithm is designed to quickly achieve the optimal PNE, which can minimize the defined networks’ interference metric. Simulation results show that the proposed algorithm outperforms the existing schemes and significantly improves the spectrum efficiency.