Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Achievable Rates of FDD Massive MIMO Systems With Spatial Channel Correlation

Published: 01 May 2015 Publication History

Abstract

It is well known that the performance of frequency-division-duplex (FDD) massive MIMO systems with i.i.d. channels is disappointing compared with that of time-division-duplex (TDD) systems, due to the prohibitively large overhead for acquiring channel state information at the transmitter (CSIT). In this paper, we investigate the achievable rates of FDD massive MIMO systems with spatially correlated channels, considering the CSIT acquisition dimensionality loss, the imperfection of CSIT and the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously designing the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, based on the one-ring model and the Laplacian model. It is shown that, based on the proposed eigenspace channel estimation schemes, the rate-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate number of base station antennas. Compared to the JSDM scheme, our proposal achieves dimensionality-reduction channel estimation without channel pre-projection, and higher throughput for moderate number of antennas and moderate to large channel coherence block length, though at higher computational complexity.

References

[1]
T. Marzetta, “ Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Trans. Wireless Commun., vol. 9, no. 11, pp. 3590– 3600, Nov. 2010.
[2]
H. Q. Ngo, E. Larsson, and T. Marzetta, “ Energy and spectral efficiency of very large multiuser MIMO systems,” IEEE Trans Commun., vol. 61, no. 4, pp. 1436– 1449, Apr. 2013.
[3]
G. Caire, and S. Shamai, “ On the achievable throughput of a multiantenna Gaussian broadcast channel,” IEEE Trans. Inf. Theory, vol. 49, no. 7, pp. 1691– 1706, Jul. 2003.
[4]
A. G. Davoodi, and S. A. Jafar, Aligned Image Sets Under Channel Uncertainty: Settling a Conjecture by Lapidoth, Shamai and Wigger on the Collapse of Degrees of Freedom Under Finite Precision CSIT, arXiv preprint arXiv:1403.1541.
[5]
G. Smith, “ A direct derivation of a single-antenna reciprocity relation for the time domain,” IEEE Trans. Antennas Propag., vol. 52, no. 6, pp. 1568– 1577, Jun. 2004.
[6]
B. Clerckx, G. Kim, and S. Kim, “ Correlated fading in broadcast MIMO channels: Curse or blessing?,” in Proc. IEEE GLOBECOM, 2008, pp. 1– 5.
[7]
A. Adhikary, J. Nam, J.-Y. Ahn, and G. Caire, “ Joint spatial division and multiplexing: The large-scale array regime,” IEEE Trans. Inf. Theory, vol. 59, no. 10, pp. 6441– 6463, Oct. 2013.
[8]
J. Nam, A. Adhikary, J.-Y. Ahn, and G. Caire, “ Joint spatial division and multiplexing: Opportunistic beamforming, user grouping and simplified downlink scheduling,” IEEE J. Sel. Top. Signal Process., vol. 8, no. 5, pp. 876– 890, Oct. 2014.
[9]
A. Adhikary, et al., “ Joint spatial division and multiplexing for mm-wave channels,” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1239– 1255, Jun. 2014.
[10]
J. Nam, Fundamental Limits in Correlated Fading MIMO Broadcast Channels: Benefits of Transmit Correlation Diversity , arXiv preprint arXiv:1401.7114, 2014.
[11]
R. Müller, L. Cottatellucci, and M. Vehkapera, “ Blind pilot decontamination,” IEEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 773– 786, Oct. 2014.
[12]
C. Studer, and E. Larsson, “ PAR-aware large-scale multi-user MIMOOFDM downlink,” IEEE J. Sel. Areas Commun., vol. 31, no. 2, pp. 303– 313, Feb. 2013.
[13]
E. Björnson, J. Hoydis, M. Kountouris, and M. Debbah, “ Massive MIMO systems with non-ideal hardware: Energy efficiency, estimation, capacity limits,” IEEE Trans. Inf. Theory, vol. 60, no. 11, pp. 7112– 7139, Sep. 2014.
[14]
C. Shepard, H. Yu, and L. Zhong, “ ArgosV2: A flexible many-antenna research platform,” in Proc. Annu. Int. Conf. Mobile Comput. Netw., 2013, pp. 163– 166.
[15]
X. Rao, and V. Lau, “ Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems,” IEEE Trans. Signal Process., vol. 62, no. 12, pp. 3261– 3271, Jun. 2014.
[16]
J. Choi, D. J. Love, and T. Kim, Trellis-Extended Codebooks and Successive Phase Adjustment: A Path From LTE-Advanced to FDD Massive MIMO Systems, arXiv preprint arXiv:1402.6794, 2014.
[17]
J. Choi, Z. Chance, D. Love, and U. Madhow, “ Noncoherent trellis coded quantization: A practical limited feedback technique for massive MIMO systems,” IEEE Trans. Commun., vol. 61, no. 12, pp. 5016– 5029, Dec. 2013.
[18]
J. Choi, D. Love, and P. Bidigare, “ Downlink training techniques for FDD massive MIMO systems: Open-loop and closed-loop training with memory,” IEEE J. Sel. Top. Signal Process., vol. 8, no. 5, pp. 802– 814, Oct. 2014.
[19]
Z. Jiang, A. F. Molisch, G. Caire, and Z. Niu, “ On the achievable rates of FDD massive MIMO systems with spatial channel correlation,” in Proc. IEEE ICCC, 2014, pp. 276– 280.
[20]
K. Hugl, J. Laurila, and E. Bonek, “ Transformation based downlink beamforming techniques for frequency division duplex systems,” in Proc. Interim Symp. Antennas Propag., 2000, pp. 1529– 1532.
[21]
W. Weichselberger, M. Herdin, H. Ozcelik, and E. Bonek, “ A stochastic MIMO channel model with joint correlation of both link ends,” IEEE Trans. Wireless Commun., vol. 5, no. 1, pp. 90– 100, Jan. 2006.
[22]
S. Jafar, S. Vishwanath, and A. Goldsmith, “ Channel capacity and beamforming for multiple transmit and receive antennas with covariance feedback,” in Proc. IEEE ICC, 2001, vol. 7, pp. 2266– 2270.
[23]
J. Li, X. Wu, and R. Laroia, OFDMA Mobile Broadband Communications: A Systems Approach, Cambridge, U.K.: Cambridge Univ. Press, 2013.
[24]
A. F. Molisch, Wireless Communications 2nd, Piscataway, NJ, USA: IEEE Press, 2011.
[25]
G. Caire, N. Jindal, M. Kobayashi, and N. Ravindran, “ Multiuser MIMO achievable rates with downlink training and channel state feedback,” IEEE Trans. Inf. Theory, vol. 56, no. 6, pp. 2845– 2866, Jun. 2010.
[26]
J. H. Kotecha, and A. Sayeed, “ Transmit signal design for optimal estimation of correlated MIMO channels,” IEEE Trans. Signal Process., vol. 52, no. 2, pp. 546– 557, Feb. 2004.
[27]
S. Boyd, and L. Vandenberghe, Convex Optimization, Cambridge, U.K.: Cambridge Univ. Press, 2009.
[28]
D. Guo, S. Shamai, and S. Verdu, “ Mutual information and minimum mean-square error in Gaussian channels,” IEEE Trans. Inf. Theory, vol. 51, no. 4, pp. 1261– 1282, Apr. 2005.
[29]
S. Serbetli, and A. Yener, “ Transceiver optimization for multiuser MIMO systems,” IEEE Trans. Signal Process., vol. 52, no. 1, pp. 214– 226, Jan. 2004.
[30]
T. M. Cover, and J. A. Thomas, Elements of Information Theory, Hoboken, NJ, USA: Wiley, 2012.
[31]
V. Raghavan, and V. Veeravalli, “ Ensemble properties of RVQ-based limited-feedback beamforming codebooks,” IEEE Trans. Inf. Theory, vol. 59, no. 12, pp. 8224– 8249, Dec. 2013.
[32]
C. K. Au-Yeung, and D. Love, “ On the performance of random vector quantization limited feedback beamforming in a MISO system,” IEEE Trans. Wireless Commun., vol. 6, no. 2, pp. 458– 462, Feb. 2007.
[33]
P. Xia, and G. Giannakis, “ Design and analysis of transmit-beamforming based on limited-rate feedback,” IEEE Trans. Signal Process., vol. 54, no. 5, pp. 1853– 1863, May 2006.
[34]
S. Lloyd, “ Least squares quantization in PCM,” IEEE Trans. Inf. Theory, vol. IT-28, no. 2, pp. 129– 137, Mar. 1982.
[35]
S. Wagner, R. Couillet, M. Debbah, and D. T. M. Slock, “ Large system analysis of linear precoding in correlated MISO broadcast channels under limited feedback,” IEEE Trans. Inf. Theory, vol. 58, no. 7, pp. 4509– 4537, Jul. 2012.
[36]
C. Peel, B. Hochwald, and A. Swindlehurst, “ A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: Channel inversion and regularization,” IEEE Trans. Commun., vol. 53, no. 1, pp. 195– 202, Jan. 2005.
[37]
M. Kobayashi, N. Jindal, and G. Caire, “ Training and feedback optimization for multiuser MIMO downlink,” IEEE Trans. Commun., vol. 59, no. 8, pp. 2228– 2240, Aug. 2011.
[38]
D. shan Shiu, G. Foschini, M. Gans, and J. Kahn, “ Fading correlation and its effect on the capacity of multielement antenna systems,” IEEE Trans. Commun., vol. 48, no. 3, pp. 502– 513, Mar. 2000.
[39]
B. Hassibi, and B. M. Hochwald, “ How much training is needed in multiple-antenna wireless links?,” IEEE Trans. Inf. Theory, vol. 49, no. 4, pp. 951– 963, Apr. 2003.
[40]
J. Ziv, “ On universal quantization,” IEEE Trans. Inf. Theory, vol. IT-31, no. 3, pp. 344– 347, May 1985.

Cited By

View all
  • (2024)Joint and Robust Beamforming Framework for Integrated Sensing and Communication SystemsIEEE Transactions on Wireless Communications10.1109/TWC.2024.345498723:11_Part_2(17602-17618)Online publication date: 1-Nov-2024
  • (2024)MU-MIMO Beamforming With Limited Channel Data SamplesIEEE Journal on Selected Areas in Communications10.1109/JSAC.2024.343151542:11(3032-3047)Online publication date: 1-Nov-2024
  • (2023)Analysis of scalable channel estimation in FDD massive MIMOEURASIP Journal on Wireless Communications and Networking10.1186/s13638-022-02199-z2023:1Online publication date: 16-Mar-2023
  • Show More Cited By

Index Terms

  1. Achievable Rates of FDD Massive MIMO Systems With Spatial Channel Correlation
            Index terms have been assigned to the content through auto-classification.

            Recommendations

            Comments

            Information & Contributors

            Information

            Published In

            cover image IEEE Transactions on Wireless Communications
            IEEE Transactions on Wireless Communications  Volume 14, Issue 5
            May 2015
            592 pages

            Publisher

            IEEE Press

            Publication History

            Published: 01 May 2015

            Author Tags

            1. feedback codebook design
            2. Massive multiple-input-multiple-output (MIMO) systems
            3. frequency-division-duplex
            4. spatial channel correlation
            5. training sequences design

            Qualifiers

            • Research-article

            Contributors

            Other Metrics

            Bibliometrics & Citations

            Bibliometrics

            Article Metrics

            • Downloads (Last 12 months)0
            • Downloads (Last 6 weeks)0
            Reflects downloads up to 16 Jan 2025

            Other Metrics

            Citations

            Cited By

            View all
            • (2024)Joint and Robust Beamforming Framework for Integrated Sensing and Communication SystemsIEEE Transactions on Wireless Communications10.1109/TWC.2024.345498723:11_Part_2(17602-17618)Online publication date: 1-Nov-2024
            • (2024)MU-MIMO Beamforming With Limited Channel Data SamplesIEEE Journal on Selected Areas in Communications10.1109/JSAC.2024.343151542:11(3032-3047)Online publication date: 1-Nov-2024
            • (2023)Analysis of scalable channel estimation in FDD massive MIMOEURASIP Journal on Wireless Communications and Networking10.1186/s13638-022-02199-z2023:1Online publication date: 16-Mar-2023
            • (2023)FDD Massive MIMO Channel Training: Optimal Rate-Distortion Bounds and the Spectral Efficiency of “One-Shot” SchemesIEEE Transactions on Wireless Communications10.1109/TWC.2023.323910722:9(6018-6032)Online publication date: 30-Jan-2023
            • (2023)A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO With MobilityIEEE Transactions on Wireless Communications10.1109/TWC.2022.321029022:4(2215-2230)Online publication date: 1-Apr-2023
            • (2022)Intelligent Reflecting Surface Assisted mmWave Communication Using Mixed Timescale Channel State InformationIEEE Transactions on Wireless Communications10.1109/TWC.2022.314276721:7(5673-5687)Online publication date: 1-Jul-2022
            • (2022)Robust Channel Estimation in Multiuser Downlink 5G Systems Under Channel UncertaintiesIEEE Transactions on Mobile Computing10.1109/TMC.2021.308439821:12(4569-4582)Online publication date: 1-Dec-2022
            • (2022)Channel State Acquisition in FDD Massive MIMO: Rate-Distortion Bound and Effectiveness of "Analog" Feedback2022 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT50566.2022.9834529(2505-2510)Online publication date: 26-Jun-2022
            • (2022)D2BF—Data-Driven Beamforming in MU-MIMO with Channel Estimation UncertaintyIEEE INFOCOM 2022 - IEEE Conference on Computer Communications10.1109/INFOCOM48880.2022.9796930(120-129)Online publication date: 2-May-2022
            • (2022)Channel estimation in massive MIMO-based wireless network using spatially correlated channel-based three-dimensional arrayTelecommunications Systems10.1007/s11235-021-00873-z79:3(323-340)Online publication date: 1-Mar-2022
            • Show More Cited By

            View Options

            View options

            Media

            Figures

            Other

            Tables

            Share

            Share

            Share this Publication link

            Share on social media