Abstract-Cloud-Radio Access Network (C-RAN), one of the prominent architectures for 5G cellular systems, 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, two baseline algorithmic solutions are studied, namely a standard C-RAN approach based on quantize-and-forward and an alternative scheme based on detect-and-forward. Both techniques leverage Bayesian compressive sensing (CS) algorithms and are elucidated with reference to the state-of-the-art on CS. Numerical results illustrate the advantages of centralized processing and the relative merits of the two schemes as a function of the system parameters.
In REF , the effect of random access in Cloud-Radio Access Network is considered.
Random Access in C-RAN for User Activity Detection with Limited-Capacity Fronthaul
ArXiv
ArXiv
Computer Science Mathematics