Interplay of energy and bandwidth consumption in CRAN with optimal function split

Abstract

Abstract-Cloud radio access network (CRAN) has been proposed as a potential energy saving architecture and a scalable solution to increase the capacity and performance of radio networks. The original CRAN decouples the digital unit (DU) from radio unit (RU) and centralizes the DUs. However, stringent delay and bandwidth constraints are incurred by fronthaul in CRAN, i.e. the network segment connecting RUs and DUs. In this study, we propose a modified CRAN architecture, namely hybrid cloud RAN (H-CRAN), where a DU's functionalities can be virtualized and split at several conceivable points. Each split option results in two-level deployment of the processing functions, i.e., central cloud level and edge cloud level, connected by a transport layer called "midhaul". We study the interplay of energy efficiency and midhaul bandwidth consumption when baseband functions are centralized at the edge cloud vs central cloud. We jointly minimize the power and midhaul bandwidth consumption in H-CRAN, while satisfying the network constraints. The addressed problem with the associated constrains are modeled as a mixed integer constraint optimization problem. Numerical results show the compromise between energy and bandwidth consumption, with the optimal placement of baseband processing functions in H-CRAN architecture.

Delay-aware green hybrid CRAN

“…In this section, we present the system/network architecture, while highlighting the distinctions against reference architectures, and function splits options, similar to [1]. The introduction of these architectures will pave the way for understanding the proposed delay model, later in Sec.…”

“…For instance, including the delay as a constraint or including the normalized delay as another component of the objective function. Before discussing each option, we recall our previously proposed multi-objective optimization, that includes minimization of energy and bandwidth [1].…”

“…V we include a numerical figure, which describes how different split decisions impact the system delay performance. 2) Incorporating the actual end-to-end delay per user request in the objective function to be minimized in combined with the normalized energy and midhaul bandwidth, as in [1]. As described in the previous point, the optimal function processing split point that minimizes the system energy might not be the optimal point for minimizing the user's request delay performance.…”

“…In this work, we utilize our previously proposed architecture of H-CRAN with two transportation links, i.e., midhaul (connects CC-to-ECs) and fronthaul (connects ECs-to-RUs) [1]. In the H-CRAN architecture, digital units (DUs) are deployed at both ECs and CC to allow processing of baseband functions in each or both of the clouds, EC and/or CC.…”

“…In addition to sharing the processing and virtualization of functions, H-CRAN provides a multiplexing gain for saving system's energy and cost. In our previous work, in [1], we have proposed and solved an optimization problem that formulates the impact of different function processing splits on the interplay between energy and bandwidth consumption in H-CRAN. Our proposed architecture contains a central cloud, i.e., which has huge computational capabilities and efficient power consumption, connected via fiber to edge clouds, i.e,.…”