Context-driven power management in cache-enabled base stations using a Bayesian neural network

Abstract

Aggressive network densification in next generation cellular networks is accompanied by an increase of the system energy consumption and calls for more advanced power management techniques in base stations. In this paper, we present a novel proactive and decentralized power management method for small cell base stations in a cache-enabled multitier heterogeneous cellular network. User contexts are utilized to drive the decision of dynamically switching a small cell base station between the active mode and the sleep mode to minimize the total energy consumption. The online control problem is formulated as a contextual multi-armed bandit problem. A variational inference based Bayesian neural network is proposed as the solution method, which implicitly finds a proper balance between exploration and exploitation. Experimental results show that the proposed solution can achieve up to 46.9% total energy reduction compared to baseline algorithms in the high density deployment scenario and has comparable performance to an offline optimal solution.