Structured Sparse Channel Estimation for 3D-MIMO Systems

In this paper, a low-complexity sparse channel estimation scheme is proposed for massive three dimensional multi-input multi-output (3D-MIMO) systems. In outdoor propagation environment, 3D-MIMO channels exhibit joint sparseness in both temporal and angular domains, sharing the common support in delay domain. By taking prior knowledge of the structured sparseness, the proposed heuristic channel estimation method can greatly reduce the complexity of channel estimation, and achieve a near optimal performance. Simulation results verify the effectiveness of the proposed algorithm.

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

[2]  Tareq Y. Al-Naffouri,et al.  Sparse Reconstruction Using Distribution Agnostic Bayesian Matching Pursuit , 2013, IEEE Transactions on Signal Processing.

[3]  Chau Yuen,et al.  Asymptotic Orthogonality Analysis of Time-Domain Sparse Massive MIMO Channels , 2015, IEEE Communications Letters.

[4]  Vincent K. N. Lau,et al.  Distributed Compressive CSIT Estimation and Feedback for FDD Multi-User Massive MIMO Systems , 2014, IEEE Transactions on Signal Processing.

[5]  Robert D. Nowak,et al.  Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels , 2010, Proceedings of the IEEE.

[6]  Shi Jin,et al.  Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning , 2015, IEEE Transactions on Wireless Communications.

[7]  Xin Sun,et al.  Efficient Downlink Channel Estimation Scheme Based on Block-Structured Compressive Sensing for TDD Massive MU-MIMO Systems , 2015, IEEE Wireless Communications Letters.

[8]  Bo Hu,et al.  From Sparse Channel to Sparse Beamforming: A 3D-MIMO Case , 2014, 2015 IEEE Global Communications Conference (GLOBECOM).

[9]  Linglong Dai,et al.  Spectrally Efficient Time-Frequency Training OFDM for Mobile Large-Scale MIMO Systems , 2013, IEEE Journal on Selected Areas in Communications.

[10]  Erik G. Larsson,et al.  Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays , 2012, IEEE Signal Process. Mag..

[11]  Ali Ghrayeb,et al.  Compressive sensing-based channel estimation for massive multiuser MIMO systems , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[12]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[13]  Yang Li,et al.  Fulfilling the promise of massive MIMO with 2D active antenna array , 2012, 2012 IEEE Globecom Workshops.

[14]  Xiang Cheng,et al.  Communicating in the real world: 3D MIMO , 2014, IEEE Wireless Communications.

[15]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[16]  Terence Tao,et al.  The Dantzig selector: Statistical estimation when P is much larger than n , 2005, math/0506081.

[17]  Boon Loong Ng,et al.  Full-dimension MIMO (FD-MIMO) for next generation cellular technology , 2013, IEEE Communications Magazine.

[18]  Thomas L. Marzetta,et al.  Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas , 2010, IEEE Transactions on Wireless Communications.

[19]  Yi Zhu,et al.  Joint angle and delay estimation for 2D active broadband MIMO-OFDM systems , 2013, 2013 IEEE Global Communications Conference (GLOBECOM).

[20]  Sundeep Rangan,et al.  Estimation of Sparse MIMO Channels with Common Support , 2011, IEEE Transactions on Communications.