Abstract
Conventional channel estimation schemes may require for the transmission of orthogonal pilot signal for each antenna in frequency-division duplex (FDD) transmission systems. It is of great concern to get channel information with an affordable signaling overhead in FDD massive multi-input multi-output (m-MIMO) transmission environments. The signaling overhead for the channel estimation can be reduced by exploiting the channel correlation matrix (CCM) of m-MIMO channel. When the m-MIMO channel is correlated in spatial domain, the channel information can be estimated with reduced pilot signaling overhead. However, the estimation performance may seriously be affected by the accuracy of CCM. In this paper, we investigate the effect of CCM accuracy on the m-MIMO channel estimation and verify it by computer simulation.
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Lee, J., et al. (2012). Coordinated multipoint transmission and reception in LTE-advanced systems. IEEE Communications Magazine, 50(11), 44–50.
Clerckx, B., et al. (2011). Coordinated multi-point transmission in heterogeneous networks: A distributed antenna system approach. In Proceedings of MWSCAS
Cisco visual networking index: Global mobile data traffic forecast update, 2012–2017. White Paper (2013)
Gesbert, D., et al. (2007). Shifting the MIMO paradigm. IEEE Signal Processing Magazine, 24(5), 36–46.
Marzetta, T. (2010). Noncooperative cellular wireless with unlimited numbers of BS antennas. IEEE Transactions Wireless Communications, 9(11), 3590–3600.
Rusek, F., et al. (2013). Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30, 40–60.
Shim, B., & Lee, B. (2013). Evolution of MIMO technology. Journal of Korean Institute Communications and Information Science, 38(8), 712–723.
Park, S., et al. (2014). Joint transmission with MRT beamforming for cell-edge users in massive MIMO systems. In proceedings of symposium of Korean institute of communication and information sciences, pp. 422–423 (2014)
Weber, T., & Meurer, M. (2004). Imperfect channel state information in MIMO-transmission. Proceedings of IEEE VTC, 2, 693–697.
Musavian, L., et al. (2007). Effect of channel uncertainty on the mutual information of MIMO fading channels. IEEE Transactions on Vehicular Technology, 56(5), 2798–2806.
Wang, J., & Palomar, D. (2009). Worst-case robust MIMO transmission with imperfect channel knowledge. IEEE Transactions on Signal Processing, 57(8), 3086–3100.
DallAnese. E., et al. (2009). On the effect of imperfect channel estimation upon the capacity of correlated MIMO fading channels, in Proceedings of IEEE VTC, pp. 1–5
Lau, V., & Kwok, Y. (2005). Channel adaptation technologies and cross layer design for wireless systems with multiple antennastheory and applications. New Jersey: Wiley John Proakis Telecom Series.
Yin, H., et al. (2013). A coordinated approach to channel estimation in large-scale multiple-antenna systems. IEEE Journal on Selected Areas in Communications, 31(2), 264–273.
Wang, X., & Wang, J. (2004). Effect of imperfect channel estimation on transmit diversity in CDMA systems. IEEE Transactions on Vehicular Technology, 53, 1400–1412.
Zhu, H. (2012). Radio resource allocation for OFDMA systems in high speed environments. IEEE Journal on Selected Areas in Communications, 30, 748–759.
Kotecha, J., & Sayeed, A. (2004). Transmit signal design for optimal estimation of correlated MIMO channels. IEEE Transactions on Signal Processing, 52, 546–557.
Lee, B., et al. (2016). Exploiting dominant eigendirections for feedback compression for FDD-based massive MIMO systems. In Proceedings of IEEE ICC, pp. 1–6
Choi, J., et al. (2014). Downlink training techniques for FDD massive MIMO systems: Open-loop and closed-loop training with memory. IEEE Journal of Selected Topics Signal Processing, 8, 802–814.
Noh, S., et al. (2014). Pilot beam pattern design for channel estimation in massive MIMO systems. IEEE Journal of Selected Topics Signal Processing, 8, 787–801.
Nam, J. (2014). Fundamental limits in correlated fading MIMO broadcast channels: Benefits of transmit correlation diversity. In Proceedings of IEEE ISIT, pp. 2889–2893
Adhikary, A., et al. (2013). Joint spatial division and multiplexingthe large-scale array regime. IEEE Transactions on Information Theory, 59, 6441–6463.
Jiang, Z., et al. (2015). Achievable rates of FDD massive MIMO systems with spatial channel correlation. IEEE Transactions on Wireless Communications, 14(5), 2868–2882.
Ngo, H., Larsson, E. (2012). EVD-based channel estimation in multicell multiuser MIMO systems with very large antenna arrays, in Proceedings of IEEE ICASSP, pp. 3249–3252
Lee, K., Lee, Y. (2008) Cooperative transmission with partial channel information in multi-user MISO wireless systems. In Proceedings of IEEE VTC, pp. 1–5
Cho, H., et al. (2010). Coordinated transmission of interference mitigation and power allocation in two-user two-hop MIMO relay systems. Journal on Wireless Communications and Networking, 2010, 1–15.
Cho, H., et al. (2011). Interference coordination based on statistical CSI in multi-user MISO cellular systems. In Proceedings of European Wireless, pp. 1–7
Li, X., et al. (2016). Three-dimensional beamforming for large-scale FD-MIMO systems exploiting statistical channel state information. IEEE Transactions on Vehicular Technology, 65(11), 8992–9005.
Liang, Y., & Chin, F. (2001). Downlink channel covariance matrix (DCCM) estimation and its applications in wireless DS-CDMA systems. IEEE Journal on Selected Areas in Communications, 19, 222–232.
Hochwald, B., & Marzetta, T. (2001). Adapting a downlink array from uplink measurements. IEEE Transactions on Signal Processing, 49(3), 642–653.
Bickel, P., & Levina, E. (2008). Regularized estimation of large covariance matrices. The Annals of Statistics, 36(1), 199–227.
Chen, Y., et al. (2010). Robust shrinkage estimation of high-dimensional covariance matrices, In IEEE sensor array and multichannel signal process. workshop, pp. 189-192
Tsiligkaridis, T., & Hero, A. (2013). Covariance estimation in high dimensions via Kronecker product expansions. IEEE Transactions on Signal Processing, 61(21), 5347–5360.
Soloveychik, I., & Wiesel, A. (2014). Tylers covariance matrix estimator in elliptical models with convex structure. IEEE Transactions on Signal Processing, 62(20), 5251–5259.
Karim, L. (2014). High-dimensional covariance matrix estimation with missing observations. Bernoulli, 20(3), 1029–1058.
Kay, S. (2000). Fundamentals of statistical signal processing: Estimation theory (1st ed.). New Jersey: Prentice Hall.
Ying, D., et al. (2014). Kronecker product correlation model and limited feedback codebook design in a 3D channel model. In Proceedings of IEEE ICC, pp. 5865–5870
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This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No.2014-0-00282, Development of 5G Mobile Communication Technologies for Hyper-connected smart services).
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Kim, HK., Byun, YS. & Lee, YH. Estimation of MIMO Channel with Imperfect Channel Correlation Information. Wireless Pers Commun 95, 3377–3389 (2017). https://doi.org/10.1007/s11277-017-4002-0
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DOI: https://doi.org/10.1007/s11277-017-4002-0