MASSIVE DEVICE ACTIVITY DETECTION BY APPROXIMATE MESSAGE PASSING

User activity detection is a central problem in massive device communication scenarios in which an access point needs to detect active devices among large number of potential devices each transmitting sporadically. By exploiting sparsity in user activity, the detection problem can be formulated as a compressed sensing problem, thereby allowing the use of computationally efficient approximate message passing (AMP) algorithm for activity detection. This paper proposes an AMP-based user activity detector that accounts for the statistics of device geographic locations in a cellular network. The proposed scheme is based on a minimum mean squared error (MMSE) denoiser designed for specific wireless channel fading and path-Ioss distributions. This paper further provides an analytic characterization of the false alarm versus missed detection probabilities using state evolution for AMP. Simulation resuIts show significantly improved detection threshold for the channel-aware denoiser as compared to standard soft threshold based AMP.