Random Access in C-RAN for User Activity Detection with Limited-Capacity Fronthaul

TL;DR

This paper investigates how fronthaul capacity limitations in C-RAN affect user activity detection, comparing centralized and local detection schemes using Bayesian sparse detection methods.

Contribution

It introduces and compares a new functional split scheme enabling local detection against the standard centralized approach in C-RANs.

Findings

Local detection performs better with limited fronthaul capacity.

Centralized detection is more effective with high-capacity fronthaul links.

Numerical results demonstrate the trade-offs between the two schemes.

Abstract

Cloud-Radio Access Network (C-RAN) is characterized by a hierarchical structure in which the baseband processing functionalities of remote radio heads (RRHs) are implemented by means of cloud computing at a Central Unit (CU). A key limitation of C-RANs is given by the capacity constraints of the fronthaul links connecting RRHs to the CU. In this letter, the impact of this architectural constraint is investigated for the fundamental functions of random access and active User Equipment (UE) identification in the presence of a potentially massive number of UEs. In particular, the standard C-RAN approach based on quantize-and-forward and centralized detection is compared to a scheme based on an alternative CU-RRH functional split that enables local detection. Both techniques leverage Bayesian sparse detection. Numerical results illustrate the relative merits of the two schemes as a function of the system parameters.