  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.
