Delay-aware green hybrid CRAN

As a potential candidate architecture for 5G systems, cloud radio access network (CRAN) enhances the system's capacity by centralizing the processing and coordination at the central cloud. However, this centralization imposes stringent bandwidth and delay requirements on the fronthaul segment of the network that connects the centralized baseband processing units (BBUs) to the radio units (RUs). Hence, hybrid CRAN is proposed to alleviate the fronthaul bandwidth requirement. The concept of hybrid CRAN supports the proposal of splitting/virtualizing the BBU functions processing between the central cloud (central office that has large processing capacity and efficiency) and the edge cloud (an aggregation node which is closer to the user, but usually has less efficiency in processing). In our previous work, we have studied the impact of different split points on the system's energy and fronthaul bandwidth consumption. In this study, we analyze the delay performance of the end user's request. We propose an end-to-end (from the central cloud to the end user) delay model (per user's request) for different function split points. In this model, different delay requirements enforce different function splits, hence affect the system's energy consumption. Therefore, we propose several research directions to incorporate the proposed delay model in the problem of minimizing energy and bandwidth consumption in the network. We found that the required function split decision, to achieve minimum delay, is significantly affected by the processing power efficiency ratio between processing units of edge cloud and central cloud. High processing efficiency ratio (≈1) leads to significant delay improvement when processing more base band functions at the edge cloud.