The 5G Cellular Backhaul Management Dilemma: To Cache or to Serve
Abstract
To reap the benefits of cache-enabled small cell networks, new backaul management mechanisms are needed to prevent the predicted files that are downloaded at the small base stations (SBSs) for caching purposes from jeopardizing the urgent requests that need to be served via the backhaul. Such mechanisms must account for the heterogeneity of the backhaul that will encompass both wireless backhaul links (at various frequency bands) and a wired backhaul component. In this paper, the heterogeneous backhaul management problem is formulated as a minority game in which each SBS has to define the number of predicted files to download, without affecting the required transmission rate of the current requests. For the formulated game, it is shown that a unique fair proper mixed Nash equilibrium (PMNE) exists. A self-organizing reinforcement learning algorithm is then proposed and shown to converge to a unique Boltzmann-Gibbs equilibrium, which approximates the desired PMNE. Simulation results show that the performance of the proposed approach can be close to that of the ideal optimal algorithm while it outperforms a centralized greedy approach in terms of the amount of data that is cached without jeopardizing the quality-of-service of current requests.