Channel vector quantization for multiuser MIMO systems aiming at maximum sum rate
Abstract
For downlink transmission in a multiuser Multiple-Input Multiple-Output (MIMO) communication system, quantized Channel State Information (CSI) is fed back to the base station in an uplink channel of finite rate. The quantized CSI is obtained via Channel Vector Quantization (CVQ) of the so-called composite channel vector, i.e., the product of the channel matrix and an estimation of the receive filter, which cannot be computed exactly at the stage of quantization because of its dependency on the finally chosen precoder. Here, the state-of-the-art approach estimates the receive filter and quantize the composite channel vector such that its Euclidean distance to the estimated composite channel vector is minimized.
In this paper, we propose an alternative CVQ method which determines the estimated receive filter vector and the quantized composite channel vector such that the resulting Signal-to-Interference-and-Noise Ratio (SINR), or an approximation thereof, is maximized. Since the SINR is related to the individual user rates, and therefore related to the sum rate of the system, the presented solution aims at maximizing the system sum rate.
Simulation results of a multiuser MIMO system with linear zero-forcing precoding show that the proposed schemes achieve significant performance improvements compared to the state-of-the-art method, especially in the low signal-to-noise ratio region.
References
[1]
G. Caire and S. Shamai, "On the achievable throughput of a multiantenna Gaussian broadcast channel," IEEE Transactions on Information Theory, vol. 49, no. 7, pp. 1691-1706, July 2003.
[2]
H. Sato, "An outer bound to the capacity region of broadcast channels (corresp.)," IEEE Transactions on Information Theory, vol. 24, no. 3, pp. 374-377, May 1978.
[3]
N. Jindal, W. Rhee, S. Vishwanath, S. A. Jafar, and A. Goldsmith, "Sum power iterative water-filling for multi-antenna Gaussian broadcast channels," IEEE Transactions on Information Theory, vol. 51, no. 4, pp. 1570-1580, April 2005.
[4]
P. Tejera, W. Utschick, G. Bauch, and J. A. Nossek, "Subchannel allocation in multiuser multiple-input multiple-output systems," IEEE Transactions on Information Theory, vol. 52, no. 10, pp. 4721-4733, October 2006.
[5]
P. Ding, D. J. Love, and M. D. Zoltowski, "Multiple antenna broadcast channels with limited feedback," in Proceedings of the 2006 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '06), vol. 4, May 2006, pp. 25-28.
[6]
P. M. Castro, M. Joham, L. Castedo, and W. Utschick, "Robust precoding for multi-user MISO systems with limited-feedback channels," in Proceedings of the ITG Workshop on Smart Antennas, March 2007.
[7]
K. K. Mukkavilli, A. Sabharwal, E. Erkip, and B. Aazhang, "On beamforming with finite rate feedback in multiple-antenna systems," IEEE Transactions on Information Theory, vol. 49, no. 10, pp. 2562- 2579, October 2003.
[8]
D. J. Love, J. Heath, R. W., W. Santipach, and M. L. Honig, "What is the value of limited feedback for MIMO channels?" IEEE Communications Magazine, vol. 42, no. 10, pp. 54-59, October 2004.
[9]
N. Jindal, "A feedback reduction technique for MIMO broadcast channels," in Proceedings of the IEEE International Symposium on Information Theory, July 2006.
[10]
Philips, "Comparison between MU-MIMO codebook-based channel reporting techniques for LTE downlink," October 2006, 3GPP TSG RAN WG1 #46bis R1-062483.
[11]
Philips, "Comparison of MU-MIMO feedback schemes with multiple UE receive antennas," January 2006, 3GPP TSG RAN WG1 #47bis R1- 070346.
[12]
G. Dietl and G. Bauch, "Linear precoding in the downlink of limited feedback multiuser MIMO systems," in Proceedings of the 2007 IEEE Global Telecommunications Conference (GLOBECOM 2007), November 2007, pp. 4359-4364.
[13]
S. Verdú, Multiuser Detection. Cambridge University Press, 1998.
[14]
M. Trivellato, F. Boccardi, and F. Tosato, "User selection schemes for MIMO broadcast channels with limited feedback," in Proceedings of the 2007 IEEE 65th Vehicular Technology Conference (VTC 2007-Spring), April 2007, pp. 2089-2093.
[15]
B. Borloz and B. Xerri, "Subspace signal-to-noise ratio maximization: the constrained stochastic matched filter," in Proceedings of the 8th International Symposium on Signal Processing and Its Applications (ISSPA 2005), vol. 2, August 2005, pp. 735-738.
[16]
L. Hentilä, P. Kyösti, M. Käske, M. Narandzic, and M. Alatossava, "MATLAB implementation of the WINNER phase II channel model ver1.1," December 2007, online available at https://www.ist-winner.org/phase_2_model.html.
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Published: 30 November 2009
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