Compressive Interference Mitigation and Data Recovery in Cloud Radio Access Networks With Limited Fronthaul

In this paper, we consider compressive interference mitigation and data recovery in cloud radio access networks (C-RANs). Specifically, we exploit the inherent sparsity to reduce the fronthaul capacity requirement for joint narrowband interference (NBI) mitigation and data recovery in the uplink of a C-RAN. As the data sent by uplink users in C-RANs and the NBI from other systems are sparse in some application scenarios, a joint NBI mitigation and data recovery problem can be naturally formulated as a compressive sensing problem. To further exploit the individual sparse structure of the information and interference, we formulate a sparse maximum likelihood estimation (MLE) problem which maximizes the associated likelihood function under individual sparsity levels (ISLs) constraints. We further adopt an alternating optimization (AO) algorithm to solve this Sparse MLE problem. Besides this, we introduce a new kind of restricted isometry property (RIP) called the ISLs-RIP and show that the normalized C-RAN measurement matrix satisfies the ISLs-RIP. Based on the ISLs-RIP, we establish the convergence conditions for the AO algorithm. Finally, we analyze the MSE satisfaction probability for the C-RAN and further obtain some design insights for C-RANs by studying the impact of some key parameters on C-RAN performance.