Compressive Sensing Multi-User Detection with Block-Wise Orthogonal Least Squares
- 1 May 2012
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15502252,p. 1-5
- https://doi.org/10.1109/vetecs.2012.6240301
Abstract
One challenging future application in digital communications is the wireless uplink transmission in sensor networks. This application is characterized by sporadic transmissions by a large number of sensors over a random multiple access channel. To reduce control signaling overhead, we propose that sensors do not transmit their activity states; instead sensor activity is detected at the receiver. As sensors have low activity probabilities, the multi-user vector is in general sparse. This enables Compressive Sensing (CS) detectors to perform joint Multi-User Detection (MUD) of activity and data, by exploiting the sparsity. Since sensors are either active or inactive for several symbol durations, block-wise CS detection can be applied to improve the activity detection. In this paper, we introduce blockwise greedy CS MUD, compare it to symbol-wise greedy CS MUD, and show that statistically independent channels for each symbol further improve the activity detection for block-wise CS detection. Herein, we use Code Division Multiple Access (CDMA) as a multiple access scheme.Keywords
This publication has 16 references indexed in Scilit:
- Near-Oracle Performance of Greedy Block-Sparse Estimation Techniques From Noisy MeasurementsIEEE Journal of Selected Topics in Signal Processing, 2011
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samplesApplied and Computational Harmonic Analysis, 2009
- Fast group sparse classificationCanadian Journal of Electrical and Computer Engineering, 2009
- The Dantzig selector: Statistical estimation when p is much larger than nThe Annals of Statistics, 2007
- Compressed sensingIEEE Transactions on Information Theory, 2006
- Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency informationIEEE Transactions on Information Theory, 2006
- Regularization and Variable Selection Via the Elastic NetJournal of the Royal Statistical Society Series B: Statistical Methodology, 2005
- Orthogonal matching pursuit: recursive function approximation with applications to wavelet decompositionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Atomic Decomposition by Basis PursuitSIAM Journal on Scientific Computing, 1998
- Matching pursuits with time-frequency dictionariesIEEE Transactions on Signal Processing, 1993