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5 - Training Design and Channel Estimation in C-RANs

from Part II - Physical-Layer Design in C-RANs

Published online by Cambridge University Press:  23 February 2017

Edited by
Tony Q. S. Quek ,
Mugen Peng ,
Osvaldo Simeone  and
Wei Yu
Tony Q. S. Quek
Affiliation:
Singapore University of Technology and Design
Mugen Peng
Affiliation:
Beijing University of Posts and Telecommunications
Osvaldo Simeone
Affiliation:
New Jersey Institute of Technology
Wei Yu
Affiliation:
University of Toronto
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Summary

Background Overview

In the era of mobile Internet, the explosive growth of data traffic imposes a heavy strain on mobile operators to manage networks efficiently while maintaining the quality of service (QoS). It is envisioned that more diverse intelligent mobile devices will be incorporated in the future fifth generation (5G) networks [1].

Cloud-Ran (C-RAN) is a novel RAN architecture, proposed by China Mobile with the aim of achieving the goal of green communication, which has demonstrated its superiority over the conventional cellular networks in terms of energy and spectral efficiencies [2]. Cloud C-RANs are still faced with challenges for successful implementation and commercial operation. One of the major key technical issues awaiting timely resolution in C-RANs is the lack of instantaneous channel state information (CSI), which is essential for beamforming and for coherent reception. As a matter of fact, nearly all the advanced technologies operated in C-RANs demand an accurate knowledge of CSI. The assumption that perfect CSI is known at the baseband unit (BBU) pool is also not practical owing to the time-varying nature of the radio channel. Consequently, developing accurate and reliable channel estimation techniques is vital to support the feasible design of C-RANs.

5.1.1 The Introduction of Channel Estimation

Channel estimation as a fundamental problem in wireless communication systems has been extensively investigated in the past few decades [3]. In general, channel estimation can be basically categorized into two kinds of strategies, i.e., training-based and non-training-based channel estimation. Training-based channel estimation is the most commonly used method owing to its flexibility and low complexity. By periodically inserting the training sequences into data frames in the time or frequency domain, the receiver can easily obtain a reliable estimate of the radio channel impulse responses by means of prior knowledge of the training sequences [4]. However, the use of training sequences will degrade the system performance in terms of the spectral and energy efficiencies, as additional spectrum resource is consumed by the training transmission. Therefore, non-training-based channel estimation is proposed as an alternative way to obtain CSI, by adequately using the intrinsic characteristics of the data signal itself instead of using training [5]. Despite the high spectral efficiency, there still exist many restrictions of non-training-based channel estimation, e.g., a slow convergence speed, high computation complexity and sensitivity to noise, which to some extent limit its implementation.

Type
Chapter
Information
Cloud Radio Access Networks
Principles, Technologies, and Applications
 , pp. 82 - 116
Publisher: Cambridge University Press
Print publication year: 2017

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References

Rost, P., “Cloud technologies for flexible 5G radio access networks,” IEEE Commun. Mag., vol. 52, no. 5, pp. 68–76, May 2014.Google Scholar
Webb, M., “Smart 2020: enabling the low carbon economy in the information age.” The Climate Group, London, 2008.
Biguesh, M. and Gershman, A., “Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals,” IEEE Trans. Signal Process., vol. 54, no. 3, pp. 884–893, March 2006.Google Scholar
Barhumi, I., Leus, G., and Moonen, M., “Optimal training design for MIMO OFDM systems in mobile wireless channels,” IEEE Trans. Signal Process., vol. 51, no. 6, pp. 1615–1624, June 2003.Google Scholar
Tugnait, J. K., “Blind estimation and equalization of MIMO channels via multidelay whitening,” IEEE J. Sel. Areas Commun., vol. 19, no. 8, pp. 1507–1519, August 2001.Google Scholar
Ding, Z. and Poor, H. V., “A general framework of precoding design for multiple two-way relaying communications,” IEEE Trans. Signal Process., vol. 61, no. 6, pp. 1531–1535, March 2013.Google Scholar
Tugnait, J. K., and Luo, W., “On channel estimation using superimposed training and first-order statistics,” IEEE Commun. Lett., vol. 7, no. 9, pp. 413–415, September 2003.Google Scholar
Gao, F., “Superimposed training based channel estimation for OFDM modulated amplify-and-forward relay networks,” IEEE Trans. Commun., vol. 59, no. 7, pp. 2029–2039, July 2011.Google Scholar
Xie, X., “Superimposed training based channel estimation for uplink multiple access relay networks,” IEEE Trans. Wireless Commun., vol. 14, no. 8, pp. 4439–4453, August 2015.Google Scholar
Kong, T. and Hua, Y., “Optimal design of source and relay pilots for MIMO relay channel estimation,” IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4438–4446, September 2011.Google Scholar
Sun, S. and Jing, Y., “Training and decodings for cooperative network with multiple relays and receive antennas,” IEEE Trans. Commun., vol. 60, no. 6, pp. 1534–1544, June 2012.Google Scholar
Shun, Z., “Segment training based individual channel estimation in one-way relay network with power allocation,” IEEE Trans. Wireless Commun., vol. 12, no. 3, pp. 1300–1309, March 2013.Google Scholar
Xie, X., “Training design and channel estimation in uplink cloud radio access networks,” IEEE Signal Process. Lett., vol. 22, no. 8, pp. 1060–1064, August 2015.Google Scholar
Hu, Q., “Segment training based channel estimation and training design in cloud radio access networks,” in Proc. IEEE ICC., London, June 2015.
Zhou, S., Muquet, B., and Giannakis, G. B., “Subspace-based blind channel estimation for block precoded space-time OFDM,” IEEE Trans. Signal Process., vol. 50, no. 5, pp. 1215–1228, May 2002.Google Scholar
Böcskei, H., Heath, R. W., and Paulraj, A. J., “Blind channel identification and equalization in OFDM-based multiantenna systems,” IEEE Trans. Signal Process., vol. 50, no. 1, pp. 96–109, January 2002.Google Scholar
Grant, M., Boyd, S. and Ye, Y., CVX: Matlab software for disciplined convex programming. Online, July 2010. Available at http://cvxr.com/cvx.Google Scholar
Giannakis, G. B., and Tepedelenlioglu, C., “Basis expansion models and diversity techniques for blind identification and equalization of time-varying channels,” Proc. IEEE, vol. 86, no. 10, pp. 1969–986, October 1998.Google Scholar
Ma, X. and Giannakis, G. B., “Maximum-diversity transmissions over doubly-selective wireless channels,” IEEE Trans. Inf. Theory., vol. 49, no. 7, pp. 1832–1840, July 2003.Google Scholar
Wang, G., “Channel estimation and training design for two-way relay networks in time-selective fading environments,” IEEE Trans. Wireless Commun., vol. 10, no. 8, pp. 2681–2691, August 2011.Google Scholar
Gao, F., Zhang, R., and Liang, Y. C., “Optimal channel estimation and training design for two-way relay networks,” IEEE Trans. Commun., vol. 57, no. 10, pp. 3024–3033, October 2009.Google Scholar
“Spatial channel model for multiple input multiple output simulations.” 3GPP TR 25.996, release 9, December 2009.
Poor, H. V., An Introduction to Signal Detection and Estimation. Second Edition. Springer, 1994.
Marshall, A. W., and Olkin, I., Inequalities: Theory of Majorization and Its Applications. Academic Press, 1979.
Choi, J., Love, D. J., and Bidigare, P., “Downlink training techniques for FDD massive MIMO systems: open-loop and closed-loop training with memory,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 802–814, October 2014.Google Scholar
Abdallah, S. and Psaromiligkos, I.N., “Semi-blind channel estimation with superimposed training for OFDM-based AF two-way relaying,” IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2468–2477, May 2014.Google Scholar
Kang, J., “Joint signal and channel state information compression for the backhaul of uplink network MIMO systems,” IEEE Trans. Wireless Commun., vol. 13, no. 3, pp. 1555–1567, March 2014.Google Scholar

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