Cloud Radio Access Networks: Uplink Channel Estimation and Downlink Precoding

TL;DR

This paper investigates optimal functional splits in cloud radio access networks at the physical layer, focusing on uplink channel estimation and downlink precoding, considering fronthaul constraints and advanced compression strategies.

Contribution

It provides an information-theoretic analysis of joint optimization of fronthaul compression and baseband processing at different network layers.

Findings

Optimal functional splits depend on fronthaul capacity and network architecture.

Joint optimization strategies improve network performance under capacity constraints.

Analysis guides practical deployment of C-RAN with limited fronthaul bandwidth.

Abstract

The gains afforded by cloud radio access network (C-RAN) in terms of savings in capital and operating expenses, flexibility, interference management and network densification rely on the presence of high-capacity low-latency fronthaul connectivity between remote radio heads (RRHs) and baseband unit (BBU). In light of the non-uniform and limited availability of fiber optics cables, the bandwidth constraints on the fronthaul network call, on the one hand, for the development of advanced baseband compression strategies and, on the other hand, for a closer investigation of the optimal functional split between RRHs and BBU. In this chapter, after a brief introduction to signal processing challenges in C-RAN, this optimal function split is studied at the physical (PHY) layer as it pertains to two key baseband signal processing steps, namely channel estimation in the uplink and channel encoding/ linear precoding in the downlink. Joint optimization of baseband fronthaul compression and of baseband signal processing is tackled under different PHY functional splits, whereby uplink channel estimation and downlink channel encoding/ linear precoding are carried out either at the RRHs or at the BBU. The analysis, based on information-theoretical arguments, and numerical results yields insight into the configurations of network architecture and fronthaul capacities in which different functional splits are advantageous. The treatment also emphasizes the versatility of deterministic and stochastic successive convex approximation strategies for the optimization of C-RANs.