Energy-Efficient and QoS Guaranteed BBU Aggregation in CRAN Based on Heuristic- Assisted Deep Reinforcement Learning

The surging mobile traffic poses serious challenges for mobile operators, one of which is the unsustainable growth caused by the high energy consumption of the massively deployed base stations (BSs). Cloud radio access network (CRAN), as a new architecture, is proposed to confront this challenge. By isolating baseband unit (BBU) from its remote radio head (RRH) in BS, the BBUs are consolidated into a common place (i.e., BBU pool). Since the “any-to-any” connection between BBUs and RRHs is realized in CRAN, low utilized BBUs can be switched to sleep mode to save energy during traffic valley, which can effectively reduce the energy consumption of CRAN. However, when a BBU enters into the sleep mode, RRHs connected with this BBU must be switched to another BBU, which would degrade the quality of service (QoS) for the RRHs. In this paper, to simultaneously guarantee low BBU energy consumption and low RRH traffic migration, both of which are interrelated and mutual restraint, we propose a deep reinforcement learning (DRL) based BBU aggregation scheme. Furthermore, to train the DRL fast and well, we introduce several heuristic algorithms to assist the DRL training. Extensive numerical evaluations show that our proposed heuristic-assisted DRL (HA-DRL) can guarantee both the low power consumption and the less traffic migration. When compared with the baselines, HA-DRL can achieve up to 18.3% cost reduction and 32.8% migrated traffic reduction with at most 8.4% higher energy consumption and attains the lowest cost for all cases considered.