Exploiting Sparsity in Channel and Data Estimation for Sporadic Multi-User Communication

Abstract:
Machine-to-Machine communication requires new physical layer concepts to meet future requirements. In previous works it has already been shown that Compressive Sensing (CS) detectors are capable of jointly detecting both activity and data in Multi-User Detection (MUD). To date, the investigations on CS based MUD have omitted the channel estimation and assumed perfect channel knowledge. However, in practical applications the channel also has to be estimated, such that the joint detection of activity and data has to be extended by channel estimation. Therefore, in this paper we investigate how to adapt several approaches to channel estimation, such that they are suitable for this joint detection and estimation. Further, we provide simulation results, showing that low overhead channel estimation can be achieved, with only a small loss in detection accuracy compared to CS based MUD for perfect channel state information.