Compressed sensing Bayes-risk detection for frame based multi-user systems

Abstract

Performing joint activity and data detection has recently gained attention for reducing signaling overhead in multi-user Machine-to-Machine Communication systems. In this context, Compressed Sensing has been identified as a good candidate for joint activity and data detection especially in scenarios where the activity probability is very low. This paper augments activity and data detection for frame based multi-user uplink scenarios where nodes are (in)active for the duration of a frame. We propose a two stage detector which first estimates the set of active nodes followed by a data detector. Our detector outperforms symbol-by-symbol Maximum a posteriori detection.