Iterative Reweighted Algorithms for Joint User Identification and Channel Estimation in Spatially Correlated Massive MTC

Joint user identification and channel estimation (JUICE) is a main challenge in grant-free massive machine-type communications (mMTC). The sparse pattern in users’ activity allows to solve the JUICE as a compressed sensing problem in a multiple measurement vector (MMV) setup. This paper addresses the JUICE under the practical spatially correlated fading channel. We formulate the JUICE as an iterative reweighted ℓ2,1-norm optimization. We develop a computationally efficient alternating direction method of multipliers (ADMM) approach to solve it. In particular, by leveraging the second-order statistics of the channels, we reformulate the JUICE problem to exploit the covariance information and we derive its ADMM-based solution. The simulation results highlight the significant improvements brought by the proposed approach in terms of channel estimation and activity detection performances.

[1]  Bhaskar D. Rao,et al.  An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem , 2007, IEEE Transactions on Signal Processing.

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

[3]  Emil Björnson,et al.  Massive MIMO with imperfect channel covariance information , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[4]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[5]  Emil Björnson,et al.  Toward Massive MIMO 2.0: Understanding Spatial Correlation, Interference Suppression, and Pilot Contamination , 2019, IEEE Transactions on Communications.

[6]  Zhi Chen,et al.  Block-Sparsity-Based Multiuser Detection for Uplink Grant-Free NOMA , 2018, IEEE Transactions on Wireless Communications.

[7]  Naveen Mysore Balasubramanya,et al.  Toward the Standardization of Grant-Free Operation and the Associated NOMA Strategies in 3GPP , 2019, IEEE Communications Standards Magazine.

[8]  Erik G. Larsson,et al.  Grant-Free Massive MTC-Enabled Massive MIMO: A Compressive Sensing Approach , 2018, IEEE Transactions on Communications.

[9]  Xiang-Gen Xia,et al.  Pilot Reuse for Massive MIMO Transmission over Spatially Correlated Rayleigh Fading Channels , 2015, IEEE Transactions on Wireless Communications.

[10]  Jun Fang,et al.  Novel Bayesian Inference Algorithms for Multiuser Detection in M2M Communications , 2017, IEEE Transactions on Vehicular Technology.

[11]  Hongtao Lu,et al.  A Fast Algorithm for Recovery of Jointly Sparse Vectors based on the Alternating Direction Methods , 2011, AISTATS.

[12]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[13]  Stephen P. Boyd,et al.  Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers , 2011, Found. Trends Mach. Learn..

[14]  Ya-Feng Liu,et al.  Covariance Based Joint Activity and Data Detection for Massive Random Access with Massive MIMO , 2019, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).

[15]  Marc E. Pfetsch,et al.  A compact formulation for the l21 mixed-norm minimization problem , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

[17]  Stephen P. Boyd,et al.  Enhancing Sparsity by Reweighted ℓ1 Minimization , 2007, 0711.1612.

[18]  Emil Björnson,et al.  Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays , 2019, ArXiv.

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

[20]  Xiqi Gao,et al.  Compressive Sensing-Based Adaptive Active User Detection and Channel Estimation: Massive Access Meets Massive MIMO , 2019, IEEE Transactions on Signal Processing.

[21]  Gert R. G. Lanckriet,et al.  A majorization-minimization approach to the sparse generalized eigenvalue problem , 2011, Machine Learning.

[22]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[23]  Wei Yu,et al.  Massive Connectivity With Massive MIMO—Part I: Device Activity Detection and Channel Estimation , 2017, IEEE Transactions on Signal Processing.

[24]  Richard G. Baraniuk,et al.  A Field Guide to Forward-Backward Splitting with a FASTA Implementation , 2014, ArXiv.