EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications

Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In recovering sparsely represented multi-user vectors, compressed sensing-based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and channel estimation (CE) should be performed before data detection. In this paper, we propose the expectation propagation-based joint AUD and CE (EP-AUD/CE) technique for mMTC networks. The EP algorithm is a Bayesian framework that approximates a computationally intractable probability distribution to an easily tractable distribution. The proposed technique finds a close approximation of the posterior distribution of the sparse channel vector. Using the approximate distribution, AUD and CE are jointly performed. We show by numerical simulations that the proposed technique substantially enhances AUD and CE performances over competing algorithms.

[1]  Wei Yu,et al.  Massive device activity detection by approximate message passing , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Ole Winther,et al.  Gaussian Processes for Classification: Mean-Field Algorithms , 2000, Neural Computation.

[3]  Sarah J. Johnson,et al.  Massive Non-Orthogonal Multiple Access for Cellular IoT: Potentials and Limitations , 2016, IEEE Communications Magazine.

[4]  Dagmar Peters-Drolshagen,et al.  Activity and Channel Estimation in Multi-User Wireless Sensor Networks , 2016, WSA.

[5]  Lawrence Carin,et al.  Bayesian Compressive Sensing , 2008, IEEE Transactions on Signal Processing.

[6]  Lei Liu,et al.  Message Passing in C-RAN: Joint User Activity and Signal Detection , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[7]  Byonghyo Shim,et al.  MAP-Based Active User and Data Detection for Massive Machine-Type Communications , 2018, IEEE Transactions on Vehicular Technology.

[8]  Byonghyo Shim,et al.  Sparse Detection With Integer Constraint Using Multipath Matching Pursuit , 2014, IEEE Communications Letters.

[9]  Carsten Bockelmann,et al.  Towards Massive Connectivity Support for Scalable mMTC Communications in 5G Networks , 2018, IEEE Access.

[10]  Daniel Hernández-Lobato,et al.  Expectation propagation in linear regression models with spike-and-slab priors , 2015, Machine Learning.

[11]  H. Vincent Poor,et al.  Application of Non-Orthogonal Multiple Access in LTE and 5G Networks , 2015, IEEE Communications Magazine.

[12]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[13]  Byonghyo Shim,et al.  Joint active user detection and channel estimation for massive machine-type communications , 2017, 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[14]  Sundeep Rangan,et al.  Vector approximate message passing , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[15]  William W. Hager,et al.  Updating the Inverse of a Matrix , 1989, SIAM Rev..

[16]  Carsten Bockelmann,et al.  Sparsity-Aware Successive Interference Cancellation with Practical Constraints , 2013, WSA.

[17]  Lei Liu,et al.  Sparse Vector Recovery: Bernoulli-Gaussian Message Passing , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[18]  Lei Liu,et al.  Convergence Analysis and Assurance for Gaussian Message Passing Iterative Detector in Massive MU-MIMO Systems , 2016, IEEE Transactions on Wireless Communications.

[19]  Evgueni A. Haroutunian,et al.  Information Theory and Statistics , 2011, International Encyclopedia of Statistical Science.

[20]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[21]  Carsten Bockelmann,et al.  Compressed sensing K-best detection for sparse multi-user communications , 2014, 2014 22nd European Signal Processing Conference (EUSIPCO).

[22]  Tarik Taleb,et al.  Machine-type communications: current status and future perspectives toward 5G systems , 2015, IEEE Communications Magazine.

[23]  Ole Winther,et al.  Bayesian Inference for Structured Spike and Slab Priors , 2014, NIPS.

[24]  David B. Dunson,et al.  Multitask Compressive Sensing , 2009, IEEE Transactions on Signal Processing.

[25]  Bhaskar D. Rao,et al.  Sparse Bayesian learning for basis selection , 2004, IEEE Transactions on Signal Processing.

[26]  P. Bromiley.  Products and Convolutions of Gaussian Probability Density Functions , 2013 .

[27]  IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond M Series Mobile , radiodetermination , amateur and related satellite services , 2015 .

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

[29]  Byonghyo Shim,et al.  Sparsity-Aware Ordered Successive Interference Cancellation for Massive Machine-Type Communications , 2018, IEEE Wireless Communications Letters.

[30]  Dong In Kim,et al.  Compressed Sensing for Wireless Communications: Useful Tips and Tricks , 2015, IEEE Communications Surveys & Tutorials.

[31]  Ying Li,et al.  Gaussian Message Passing for Overloaded Massive MIMO-NOMA , 2018, IEEE Transactions on Wireless Communications.

[32]  George Eastman House,et al.  Sparse Bayesian Learning and the Relevan e Ve tor Ma hine , 2001 .

[33]  Byonghyo Shim,et al.  Multiuser Detection via Compressive Sensing , 2012, IEEE Communications Letters.

[34]  Georgios B. Giannakis,et al.  Exploiting Sparse User Activity in Multiuser Detection , 2011 .

[35]  Walid Saad,et al.  Toward Massive Machine Type Cellular Communications , 2017, IEEE Wireless Communications.

[36]  Sundeep Rangan,et al.  Vector approximate message passing for the generalized linear model , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[37]  Wei Yu,et al.  Sparse Activity Detection for Massive Connectivity , 2018, IEEE Transactions on Signal Processing.

[38]  Byonghyo Shim,et al.  Expectation Propagation-Based Active User Detection and Channel Estimation for Massive Machine-Type Communications , 2018, 2018 IEEE International Conference on Communications Workshops (ICC Workshops).

[39]  Li Ping,et al.  Orthogonal AMP , 2016, IEEE Access.

[40]  Haris Vikalo,et al.  Sparsity-Aware Sphere Decoding: Algorithms and Complexity Analysis , 2014, IEEE Transactions on Signal Processing.

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