Compressive spatial channel sounding
Loading...
Date
2018
Journal Title
Journal ISSN
Volume Title
Type
Article
Publisher
IET Digital Library
Series Info
Doi
Scientific Journal Rankings
Abstract
In this paper we investigate the application of
Compressed Sensing (CS) to MIMO channel sounding in the
spatial domain. A compressive spatial channel sounder is proposed and evaluated based on real scenarios showing advantages
in terms of time, hardware complexity and resolution. In particular, in the case where we use time division duplex for measuring the MIMO channel (in the form of antenna switching at the
transmitter and/or the receiver), the proposed approach reduces
the total number of switching periods, which implies a reduced
channel acquisition time and thus an improved Doppler bandwidth. Alternatively, if we use multiple receive RF chains for the
measurement, the compression allows to reduce the number of
RF chains, which is a relevant advantage in terms of the overall
receiver complexity, the amount of data to be processed in the
digital domain (e.g., FPGA), power consumption, as well as RF
hardware calibration. On the other hand, for the same measurement time and/or hardware complexity, one can increase
the number of array elements to cover a larger aperture and so
achieving better performance in terms of resolution.
Description
MSA Google Scholar
Keywords
University of Compressive Sensing, DOA Estimation, Channel Sounding
Citation
[1] R. S. Thoma, M. Landmann, G. Sommerkorn, and A. Richter, “Multi- ¨ dimensional high-resolution channel sounding in mobile radio,” in Instrumentation and Measurement Technology Conference, vol. 1, May 2004, pp. 257–262 Vol.1. [2] H. Krim and M. Viberg, “Two decades of array signal processing research: the parametric approach,” IEEE Signal Processing Magazine, vol. 13, no. 4, pp. 67–94, Jul 1996. [3] D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. [4] E. J. Candes, “Compressive sampling,” International Congress of Math- ´ ematicians, Madrid, Spain, European Mathematical Society, Tech. Rep., 2006. [5] E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: ´ exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, Feb 2006. [6] J. I. Tamir, T. S. Rappaport, Y. C. Eldar, and A. Aziz, “Analog compressed sensing for RF propagation channel sounding,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 2012, pp. 5317–5320. [7] M. Mishali, Y. C. Eldar, and A. J. Elron, “Xampling: Signal acquisition and processing in union of subspaces,” IEEE Transactions on Signal Processing, vol. 59, no. 10, pp. 4719–4734, Oct 2011. [8] M. Rossi, A. M. Haimovich, and Y. C. Eldar, “Spatial compressive sensing in MIMO radar with random arrays,” in Annual Conference on Information Sciences and Systems (CISS), March 2012, pp. 1–6. [9] W. Bajwa, J. Haupt, A. Sayeed, and R. Nowak, “Compressed channel sensing: A new approach to estimating sparse multipath channels,” in Proceedings of the IEEE, vol. 98, Jun 2010, pp. 1058–1067. [10] A. Richter and R. S. Thoma, “Parametric modeling and estimation of ¨ distributed diffuse scattering components of radio channels,” in COST 273, Prague, vol. 198, Sept 2003, pp. 24–26. [11] M. Steinbauer, A. F. Molisch, and E. Bonek, “The double-directional radio channel,” IEEE Antennas and Propagation Magazine, vol. 43, no. 4, pp. 51–63, Aug 2001. [12] H. L. Van Trees, Detection, estimation, and modulation theory. Part IV., Optimum array processing. New York: Wiley-Interscience, 2002. [13] D. Malioutov, M. Cetin, and A. S. Willsky, “A sparse signal reconstruction perspective for source localization with sensor arrays,” IEEE Transactions on Signal Processing, vol. 53, no. 8, pp. 3010–3022, Aug 2005. [14] A. Gretsistas and M. D. Plumbley, A Multichannel Spatial Compressed Sensing Approach for Direction of Arrival Estimation. Springer Berlin Heidelberg, 2010, pp. 458–465. [15] P. Stoica, P. Babu, and J. Li, “Spice: A sparse covariance-based estimation method for array processing,” IEEE Transactions on Signal Processing, vol. 59, no. 2, pp. 629–638, Feb 2011. [16] J. Gu, W. Zhu, and M. N. S. Swamy, “Compressed sensing for DOA estimation with fewer receivers than sensors,” in IEEE International Symposium on Circuits and Systems (ISCAS), May 2011, pp. 1752– 1755. [17] Y. Wang, G. Leus, and A. Pandharipande, “Direction estimation using compressive sampling array processing,” in IEEE/SP Workshop on Statistical Signal Processing, Aug 2009, pp. 626–629. [18] M. Ibrahim, V. Ramireddy, A. Lavrenko, J. Konig, F. R ¨ omer, M. Land- ¨ mann, M. Grossmann, G. D. Galdo, and R. S. Thoma, “Design and ¨ analysis of compressive antenna arrays for direction of arrival estimation,” Signal Processing, vol. 138, no. Supplement C, pp. 35 – 47, 2017. [19] M. Ibrahim, F. Romer, and G. D. Galdo, “On the design of the measure- ¨ ment matrix for compressed sensing based doa estimation,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), April 2015, pp. 3631–3635. [20] R. S. Thoma, M. Landmann, and A. Richter, “Rimax: A maximum like- ¨ lihood framework for parameter estimation in multidimensional channel sounding,” in International Symposium on Antennas and Propagation (ISAP), 2004, pp. 53–56. [21] C. Schneider, M. Kaske, G. Sommerkorn, R. S. Thom ¨ a, A. Roivainen, ¨ J. Meinila, and V. Tervo, “Directional analysis of multipath propagation ¨ in vehicle-2-vehicle channels,” in European Conference on Antennas and Propagation (EuCAP), April 2016, pp. 1–5.