Heidrun Grob-Lipski

Radio base stations in the cloud

During the past few years, architectures for cellular radio networks like 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) have evolved towards flatter topologies with fewer centralized nodes, shifting more and more radio-related processing towards the base station or even further into so-called remote radio heads near the antennas. With Internet Protocol (IP) traffic growth, user traffic lost its predictability, which resulted in the need for the over-dimensioning of processing and forwarding resources in the radio path. This is especially true in upcoming picocell and femtocell configurations in heterogeneous cellular architectures. To hold down costs resulting from these emerging architectures and deployments, we developed a concept of a cloud base station. Developed as a candidate for a lightRadio™ evolution, this cloud base station leverages virtualization techniques such as dynamic and elastic allocation of baseband processing resources between small and large cell sites, as well as load balancing between temporarily overloaded sites and their less utilized neighbors. A cloud base station in a virtualized radio access network (RAN) can serve to ease a smooth standards migration towards LTE-Advanced with the same deployed equipment, and in the future, support the transformation of dedicated baseband hardware into general purpose processing platforms. In this paper, we discuss the tight networking requirements of a cloud base station in a virtual radio access network, architecture challenges, and our cloud management solutions. We also demonstrate research results on the concept, provide validation based on simulations, and discuss business case related implications.

Multiplexing gains achieved in pools of baseband computation units in 4G cellular networks

The tremendous increase of mobile user traffic load within the last few years forces us to efficiently use the wireless network and processing resources. Cloud computing and virtualization techniques offer an exciting opportunity to considerably reduce operation costs and provide flexible and dynamic systems. In this paper we present a simulation study for a cloud base station, which concentrates baseband processing functions of multiple radio sites. There we focus on the multiplexing-gains induced from user load and traffic heterogeneity. Our simulation results show that the data traffic influences the variance of the compute resource utilization, which in consequence leads to significant multiplexing gains if multiple sectors are aggregated into one single cloud base station. In addition, the spatial user distribution has a high impact on the compute resource load. These findings should be taken into account for the assessment of multiplexing gains in real networks.

Enabling Cloud Connectivity Using SDN and NFV Technologies

Cloud environments play an important role for network and service providers. Cloud network providers require ubiquitous, broadband and minimum-delay connectivity from network providers. There are different realizations of cloud connectivity based on the Software Defined Networking (SDN) and the Network Function Virtualization (NFV) paradigm. In this paper we introduce a new concept based on the OConS architecture developed within the SAIL FP7 project. Our advanced connectivity concept focuses on interdomain connectivity.

Task assignment strategies for pools of baseband computation units in 4G cellular networks

A promising architecture for future cellular mobile networks is to place remote radio heads on the cell towers and connect those via fibers with a centralized pool of baseband units. Among other things, the centralization of baseband computation facilitates multiplexing gains and can thereby save compute resources. To realize these gains, an efficient and load-balancing assignment of compute jobs to computation units is required. In contrast to approaches in literature, our architecture virtualizes the processing for each UE separately. It thereby provides a finer granularity and allows to newly decide the assignment of a compute job to a processing unit whenever a UE starts transmitting data. In this publication, we present multiple heuristics to decide this assignment. We compare the efficiency of the assignment and the perceived service quality realized by the heuristics with an ideal assignment and the classical static cell-based assignment. Our evaluation shows that by using a good assignment heuristic, about 50% of the hardware resources can be saved.

Reallocation strategies for user processing tasks in future cloud-RAN architectures

In this paper we evaluate strategies to reduce the required processing capacity in a Cloud-Radio Access Network (C-RAN) architecture by improving the placement of user processing tasks. Our approach of assigning compute tasks in a pool of compute resources is based on fine granular tasks, where one compute task per served user is introduced. We compare different strategies in order to balance the load in the pool and save processing resources. Therefore we evaluate the best possible reallocation method by formulating an optimization problem including extensions to reduce the number of reassignments. We also introduce an algorithm for dynamic reallocations that can be implemented in real systems. From the evaluation results we can conclude that all strategies reduce the total overload by enhanced load balancing. Further all strategies improve the perceived Quality of Experience (QoE) of individual users.