Block Sparse Bayesian Learning Based Joint User Activity Detection and Channel Estimation for Grant-Free NOMA Systems
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Jiangtao Xi | Yuanyuan Zhang | Nan Wu | Qinghua Guo | Zhongyong Wang | J. Xi | Qinghua Guo | N. Wu | Zhongyong Wang | Yuanyuan Zhang
[1] Jian Li,et al. Computationally efficient sparse bayesian learning via belief propagation , 2009 .
[2] Mohammed Al-Imari,et al. Low Density Spreading for next generation multicarrier cellular systems , 2012, 2012 International Conference on Future Communication Networks.
[3] Zhi Chen,et al. Low-Complexity LDS-CDMA Detection Based on Dynamic Factor Graph , 2016, 2016 IEEE Globecom Workshops (GC Wkshps).
[4] George K. Karagiannidis,et al. A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends , 2017, IEEE Journal on Selected Areas in Communications.
[5] Zhiguo Ding,et al. Nonorthogonal Multiple Access for 5G , 2018, 5G Networks: Fundamental Requirements, Enabling Technologies, and Operations Management.
[6] Yifei Yuan,et al. Compressive Sensing Based Multi-User Detection for Uplink Grant-Free Non-Orthogonal Multiple Access , 2015, 2015 IEEE 82nd Vehicular Technology Conference (VTC2015-Fall).
[7] Robert L. Frank,et al. Phase shift pulse codes with good periodic correlation properties (Corresp.) , 1962, IRE Trans. Inf. Theory.
[8] Michael I. Jordan,et al. A generalized mean field algorithm for variational inference in exponential families , 2002, UAI.
[9] Branislav M. Popovic,et al. Generalized chirp-like polyphase sequences with optimum correlation properties , 1992, IEEE Trans. Inf. Theory.
[10] Bhaskar D. Rao,et al. Sparse Bayesian learning using approximate message passing , 2014, 2014 48th Asilomar Conference on Signals, Systems and Computers.
[11] X. Jin. Factor graphs and the Sum-Product Algorithm , 2002 .
[12] Huaping Liu,et al. Approximate Message Passing-Based Joint User Activity and Data Detection for NOMA , 2017, IEEE Communications Letters.
[13] Bernard Henri Fleury,et al. Low Complexity Sparse Bayesian Learning for Channel Estimation Using Generalized Mean Field , 2014 .
[14] Muhammad Ali Imran,et al. Fast convergence and reduced complexity receiver design for LDS-OFDM system , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).
[15] David C. Chu,et al. Polyphase codes with good periodic correlation properties (Corresp.) , 1972, IEEE Trans. Inf. Theory.
[16] Alireza Bayesteh,et al. Blind detection of SCMA for uplink grant-free multiple-access , 2014, 2014 11th International Symposium on Wireless Communications Systems (ISWCS).
[17] Alireza Bayesteh,et al. Uplink contention based SCMA for 5G radio access , 2014, 2014 IEEE Globecom Workshops (GC Wkshps).
[18] Reza Hoshyar,et al. Novel Low-Density Signature for Synchronous CDMA Systems Over AWGN Channel , 2008, IEEE Transactions on Signal Processing.
[19] Carsten Bockelmann,et al. Massive machine-type communications in 5g: physical and MAC-layer solutions , 2016, IEEE Communications Magazine.
[20] Tamás Borsos,et al. Traffic analysis of mobile broadband networks , 2007, WICON '07.
[21] Richard G. Baraniuk,et al. Bayesian Compressive Sensing Via Belief Propagation , 2008, IEEE Transactions on Signal Processing.
[22] Muhammad Ali Imran,et al. On Receiver Design for Uplink Low Density Signature OFDM (LDS-OFDM) , 2012, IEEE Transactions on Communications.
[23] Huaping Liu,et al. Near-Optimum Sparse Channel Estimation Based on Least Squares and Approximate Message Passing , 2017, IEEE Wireless Communications Letters.
[24] Petar Popovski,et al. The METIS 5G System Concept: Meeting the 5G Requirements , 2016, IEEE Communications Magazine.
[25] Zhi Chen,et al. Efficient Multi-User Detection for Uplink Grant-Free NOMA: Prior-Information Aided Adaptive Compressive Sensing Perspective , 2017, IEEE Journal on Selected Areas in Communications.
[26] Justin Dauwels,et al. On Variational Message Passing on Factor Graphs , 2007, 2007 IEEE International Symposium on Information Theory.
[27] Robert W. Heath,et al. Five disruptive technology directions for 5G , 2013, IEEE Communications Magazine.
[28] Alireza Bayesteh,et al. SCMA Codebook Design , 2014, 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall).
[29] Linglong Dai,et al. Dynamic Compressive Sensing-Based Multi-User Detection for Uplink Grant-Free NOMA , 2016, IEEE Communications Letters.
[30] Hosein Nikopour,et al. Sparse code multiple access , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).
[31] Yonina C. Eldar,et al. Block-Sparse Signals: Uncertainty Relations and Efficient Recovery , 2009, IEEE Transactions on Signal Processing.
[32] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[33] Yan Chen,et al. Sparse code multiple access: An energy efficient uplink approach for 5G wireless systems , 2014, 2014 IEEE Global Communications Conference.
[34] Bhaskar D. Rao,et al. Extension of SBL Algorithms for the Recovery of Block Sparse Signals With Intra-Block Correlation , 2012, IEEE Transactions on Signal Processing.
[35] Qinghua Guo,et al. Low complexity sparse Bayesian learning using combined belief propagation and mean field with a stretched factor graph , 2016, Signal Process..
[36] Byonghyo Shim,et al. Multiuser Detection via Compressive Sensing , 2012, IEEE Communications Letters.