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-loss distributions. This paper further provides an analytic characterization of the false alarm versus missed detection probabilities using state evolution for AMP. Simulation results show significantly improved detection threshold for the channel-aware denoiser as compared to standard soft threshold based AMP.

[1]  Philip Schniter,et al.  Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem , 2011, IEEE Transactions on Signal Processing.

[2]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

[3]  Alexander Jung,et al.  Joint channel estimation and activity detection for multiuser communication systems , 2015, 2015 IEEE International Conference on Communication Workshop (ICCW).

[4]  Lei Zhang,et al.  Neighbor discovery for wireless networks via compressed sensing , 2010, Perform. Evaluation.

[5]  Sundeep Rangan,et al.  A sparsity detection framework for on-off random access channels , 2009, 2009 IEEE International Symposium on Information Theory.

[6]  Gitta Kutyniok,et al.  Theory and applications of compressed sensing , 2012, 1203.3815.

[7]  Sundeep Rangan,et al.  Generalized approximate message passing for estimation with random linear mixing , 2010, 2011 IEEE International Symposium on Information Theory Proceedings.

[8]  Andrea Montanari,et al.  Graphical Models Concepts in Compressed Sensing , 2010, Compressed Sensing.

[9]  Andrea Montanari,et al.  Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising , 2011, IEEE Transactions on Information Theory.

[10]  Gerhard Wunder,et al.  Compressive Random Access Using a Common Overloaded Control Channel , 2015, 2015 IEEE Globecom Workshops (GC Wkshps).

[11]  Richard G. Baraniuk,et al.  Asymptotic Analysis of Complex LASSO via Complex Approximate Message Passing (CAMP) , 2011, IEEE Transactions on Information Theory.

[12]  Holger Boche,et al.  Sparse Signal Processing Concepts for Efficient 5G System Design , 2014, IEEE Access.

[13]  Andrea Montanari,et al.  Message-passing algorithms for compressed sensing , 2009, Proceedings of the National Academy of Sciences.

[14]  Carsten Bockelmann,et al.  Exploiting Sparsity in Channel and Data Estimation for Sporadic Multi-User Communication , 2013, ISWCS.

[15]  Andrea Montanari,et al.  Message passing algorithms for compressed sensing: I. motivation and construction , 2009, 2010 IEEE Information Theory Workshop on Information Theory (ITW 2010, Cairo).

[16]  Andrea Montanari,et al.  The dynamics of message passing on dense graphs, with applications to compressed sensing , 2010, 2010 IEEE International Symposium on Information Theory.

[17]  Xiao Xu,et al.  Active user detection and channel estimation in uplink CRAN systems , 2015, 2015 IEEE International Conference on Communications (ICC).