Compressive Channel Estimation and User Activity Detection in Distributed-Input Distributed-Output Systems

We address the cloud radio access network with wireless fronthaul links for massive machine-type communication as a distributed-input distributed-output (DIDO) system for simplicity. In this letter, the channel estimation and user activity detection problems in the DIDO system are studied. We notice that there are two types of sparsity in DIDO systems: The first is the sparsity of user equipment (UE) activities, and the second is the spatial sparsity of UE signals. In response, a two-stage compressed sensing process is proposed in which UE activities and the overall channel states from active UEs to the baseband unit pool are identified at the first stage, and channel states from active UEs to remote radio heads are estimated at the second stage. A low-complexity method is proposed to accelerate the process in the first stage. Simulation results are also presented to show the performance of the proposed approach.