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

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.

arXiv comment6 pages, 3 figures, under revision in IEEE Signal Processing Letters

References in corpus (3)

• Message Passing Algorithms for Compressed Sensing
• Message-Passing Estimation from Quantized Samples
• On Convergence of Approximate Message Passing

Cited by in corpus (7)

• Sparse Signal Processing for Grant-Free Massive Connectivity: A Future Paradigm for Random Access Protocols in the Internet of Things
• Multi-Cell Sparse Activity Detection for Massive Random Access: Massive MIMO versus Cooperative MIMO
• A Dimension Reduction-Based Joint Activity Detection and Channel Estimation Algorithm for Massive Access
• Hybrid Approximate Message Passing
• Sparse Activity Detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading
• Joint Source-Channel Coding for Semantics-Aware Grant-Free Radio Access in IoT Fog Networks
• Performance Analysis of a Cooperative Wireless Network with Adaptive Relays