BBU functions split and placement problem

The key obstacle in the adoption of C-RAN architecture, where the comput- ing resources (BBUs) are decoupled from cell sites (RRHs), is the high-bandwidth constraints and low-latency requirements on the fronthaul network which connects the antennas (RRHs) to the edge data centers (BBU pools). In fact, authors in [7] investigate the cloudification of RAN by characterizing baseband processing times under different conditions. They underline that C-RAN architecture should take into account the fronthaul capacity constraints, the latency requirements for baseband processing and finally the execution environment (servers and operating systems). The strong latency requirements on the fronthaul network could be reduced by split- ting the processing of baseband functions between RRHs and BBUs. In this context, several functional split options have been proposed by different organizations, e.g. NGMN [77] and 3GPP [78], in order to find good trade-off between BBU functions

centralization and fronthaul network requirements [77].

Figure 2.6: 3GPP functional split options

The 3GPP split proposal is shown in Figure 2.6, in which each functional split option represents a separation point between the layers that will be located in the cell sites (RRHs) and those located in the BBU Pools (centralized data centers). We recall that the main motivation to split the processing of BBU functions between RRH and BBU is to achieve the largest possible extent of centralization that a network architecture can allow.

Among the different functional split options depicted in Figure 2.6, our study focuses on the following four options which are illustrated in Figure 2.7 for a better understanding.

• Split 1 represents the traditional RAN architecture where PHY, RLC, MAC and PDCP functions are co-located in the cell sites (option 1 in Figure 2.6). • Split 2 is a partial centralization of BBU functions where all functions in

PHY, MAC and RLC layers are located in the cell sites and PDCP layer in the centralized data center (Option 2 in Figure 2.6).

• Split 3 is a partial centralization where PHY layer are located in RRHs and all functions in MAC, RLC and PDCP layers are incorporated in BBU pool (Option 6 in Figure 2.6).

• Split 4 represents fully-centralized architecture in which PHY, MAC , RLC and PDCP functions are moved to the BBU pools for centralized processing (Option 8 in Figure 2.6).

In the split 1, all functions in PHY, RLC, MAC and PDCP layers are located in the cell sites (RRH). Consequently, the latency constraints are less stringent which decreases its dependency on expensive fronthaul technology. However, benefits of virtualization and centralized multi-cell processing will be less. Contrary to split 1, split 4 represents the fully-centralized architecture, considered in the first C-RAN configuration, in which all functions in PHY, MAC, RLC and PDCP layers are moved to the BBU pool for centralized processing. Authors in [13] showed that the main benefit of this BBU function split option is its flexibility to support resource sharing and its efficiency to reduce energy consumption, while the disadvantage is the high data rate in fronthaul links expected for processing heterogeneous demands from multiple RRHs. In [79], authors highlighted, using a real test bed, the efficiency of the split option 4 in finding good trade-off between the processing of BBU func- tions in centralized data centers and the fronthaul requirements. In [80], authors

RRH (antenna)

Centralized data center (BBU pool) PHY MAC RLC PDCP Split 1 PHY MAC RLC PDCP Split 2 PHY MAC RLC PDCP Split 3 PHY MAC RLC PDCP Split 4

Figure 2.7: Overview of considered functional split options

confirmed that the maximum multiplexing gain on BBU resources can be achieved when considering the fully centralized C-RAN (split 4) after analyzing the differ- ent function splits. Nevertheless, split 4 requires high fronthaul network capacity compared to other split options such as Split 2. In fact, in Split 2, the PHY, MAC and RLC layers are integrated into RRHs while the PDCP layer is moved to the BBU pool to achieve increased multiplexing gains. This is feasible since functions in PDCP layer are less capacity-intensive and not subject to real-time constraints. Another BBU function split option that does not require high capacity on fronthaul network is Split 3 that consists in locating the PHY layer in the cell sites and moving other functions of upper layers to the centralized data centers. Obviously, both split options 2 and 3 have less fronthaul throughput compared to the split 4 in which the processing of baseband functions is centralized in BBU pool. However, the inter-cell collaborative processing cannot be efficiently supported when considering split 3 due to the the difficulty of interconnection between PHY layers and other layers.

Based on these different functional split options, many works investigated new approaches to determine the optimal placement of baseband processing functions in C-RAN architecture. In [81], authors proposed a low-cost solution by considering dual-site processing where baseband functions are divided between the antenna (or radio unit) and the data center (or digital unit). They provided an estimation of processing and bandwidth requirements for each functional split option and the total costs of ownership including equipment, civil work, commissioning as well as operational costs such as electricity and maintenance. The authors of [81] claimed that there is no ”one-size-fits-all” solution for functional split and they indicated that hybrid RAN deployments contribute to Total Cost of Ownership (TCO) savings when considering 5G configurations. Authors in [82] proposed a methodology to derive guidelines for minimizing capital expenditure of C-RAN due to deployment of fronthaul and baseband processing resources. This work minimizes the total length of fiber while maximizing the multiplexing gain for each shared edge cloud hosting the baseband functions. The authors in [83] proposed a model representing the baseband processing functions as a directed graph where nodes represent different

processing functions and arcs represent connectivity between them. Then, they introduced the placement of these functions on data centers located at different sites. Computational costs and transport (link) costs are defined as well as constraints on the delay (latency) incurred both for processing and transport. In this paper, the problem of finding optimum locations to place baseband functions is equivalent to finding an optimum clustering scheme for the graph nodes with the objective of minimizing the total cost while ensuring that latency constraints are met.