Subspace-Based Approaches for Hybrid Millimeter-Wave Channel Estimation
Millimeter wave communication (mmWC) is a promising volunteer for 5G communication systems with high data rates. To subdue the channel propagation characteristics in this frequency band, high dimensional antenna arrays need to be deployed at transceiver. Employing such a deployment, prevents to use of ADC or RF chain in each branch of MIMO system because of power constraints. Thus, such systems impose to have a hybrid analog/digital precoding/combining architecture. Hence, channel estimation revision seems to be essential. This paper propose new algorithms to estimate the mmW channel by exploiting the sparse nature of the channel and finding the subspace of received signal vectors based on MUSIC. By combining the multiple measurement vector (MMV) concept, MISIC , subspace augmentation (SA) and two-stage orthogonal subspace matching pursuit (TOSMP) approaches, we try to recover the indices of non-zero elements of an unknown channel matrix accurately even under the defective- rank condition. These indices are called support in the context. Simulation results indicate MUSIC-based approaches offer lower estimation error and higher sum rates compared with conventional MMV solutions.
decomposition for hybrid analog-digital millimetre-wave MIMO systems," in 2015 IEEE 16th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2015, pp. 395399.  J. D. Blanchard, M. Cermak, D. Hanle, and J. Yirong, "Greedy Algorithms for Joint Sparse Recovery," Signal Processing, IEEE Transactions on, vol. 62, pp. 1694-1704, 2014.  K. Jong Min, L. Ok Kyun, and Y. Jong Chul, "Compressive MUSIC: Revisiting the Link Between Compressive Sensing and Array Signal Processing," Information Theory, IEEE Transactions on, vol. 58, pp. 278-301, 2012.  M. E. Davies and Y. C. Eldar, "Rank awareness in joint sparse recovery," Information Theory, IEEE Transactions on, vol. 58, pp. 1135-1146, 2012.  K. Lee, Y. Bresler, and M. Junge, "Subspace methods for joint sparse recovery," Information Theory, IEEE Transactions on, vol. 58, pp. 3613-3641, 2012.  M. S. Dastgahian and H. Khoshbin, "Rankdefective Millimeter-Wave Channel Estimation Based on Subspace-Compressive Sensing," Digital Communications and Networks, 2016.  C. D. Meyer, Matrix analysis and applied linear algebra: Siam, 2000.  A. M. Sayeed, "Deconstructing multiantenna fading channels," Signal Processing, IEEE Transactions on, vol. 50, pp. 2563-2579, 2002.  Y. C. Eldar and G. Kutyniok, Compressed sensing: theory and applications: Cambridge University Press, 2012.
9 Volume 9- Number 4 – Autumn 2017
 S. Foucart, "Recovering jointly sparse vectors via hard thresholding pursuit," Proc. Sampling Theory and Applications (SampTA)],(May 2-6 2011), 2011.  J. A. Tropp, "Algorithms for simultaneous sparse approximation. Part II: Convex relaxation," Signal Processing, vol. 86, pp. 589-602, 2006.  R. Gribonval, H. Rauhut, K. Schnass, and P. Vandergheynst, "Atoms of all channels, unite! Average case analysis of multichannel sparse recovery using greedy algorithms," Journal of Fourier analysis and Applications, vol. 14, pp. 655-687, 2008.  P. Feng, "Universal minimum-rate sampling and spectrum-blind reconstruction for
multiband signals," University of Illinois at Urbana-Champaign, 1998.  J. D. Blanchard and M. E. Davies, "Recovery Guarantees for Rank Aware Pursuits," Signal Processing Letters, IEEE, vol. 19, pp. 427430, 2012.  P. Å. Wedin, "On angles between subspaces of a finite dimensional inner product space," in Matrix Pencils, ed: Springer, 1983, pp. 263-285.  Z. Zhou, J. Fang, L. Yang, H. Li, Z. Chen, and S. Li, "Channel estimation for millimeterwave multiuser MIMO systems via PARAFAC decomposition," IEEE Transactions on Wireless Communications, vol. 15, pp. 7501-7516, 2016.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)