Introduction

The trend of increased centralization holds the potential to transform mobile networks in two ways. First, centralization enables the exploitation of common channel knowledge, which in turn allows for significant improvements in the performance of a communication channel by, for instance, performing the joint transmission and reception of signals or allocating resources jointly amongst adjacent cells [1]. Second, centralized processing leverages the trend towards deploying mobile networks on low-cost commodity hardware that is running commodity or open-source software solutions. Deploying software-based implementations increases implementation flexibility, reduces service-creation time, and enables the flexible usage of processing resources through virtualization. In this chapter we use the term Cloud-RAN (C-RAN) to refer to a flexible use of commodity solutions that combines gains in both the telecommunication and information technology domains.

Before implementing the protocol stack of a RAN on a cloud-computing platform, we must also take the required effort into account, e.g., commodity hardware is considered to be less performant and energy efficient than dedicated hardware such as ASIC, DSP, or FPGA. Furthermore, resource virtualization implies an overbooking of resources while satisfying joint resource requirements of all processed base stations (BSs), which is in contrast with fulfilling individual processing constraints at each BS. Centralized signal processing may further impose stringent requirements on the fronthaul network between a radio access point (RAP) and the data center.

So far, research in the area of Cloud-RAN has focused on the telecommunication domain, e.g., the applicability of joint processing approaches, gains from centralization, and optimal degrees of centralization under different side constraints. In this chapter the focus is on the impact of limiting and virtualizing the data processing resources on the communication rate, i.e., the quantitative coupling of the required computational resources and communication rates [2]. After introducing basic notation and definitions, we consider metrics and an analytical framework that allows one to determine the data processing demand Interestingly, the data processing requirements depend not only on the number of information bits but also to a large extent on the quality of a user's communication channel. In this chapter we discuss and quantify multi-user gains, which lower the requirements on the data processing resources to be provided.