Performance of Multi-User MIMO Precoding with Limited Feedback over Measured Channels

In multi-user multiple-input multiple-output (MU-MIMO) systems, channel state information at the transmitter (CSIT) allows for multi-user spatial multiplexing and thus increases the system throughput. We assume that CSIT is obtained by means of a finite-rate feedback channel through channel vector quantization (CVQ) at the receiver. In this paper we use real channel measurements to study the effect of CVQ on the sum rate of a MU-MIMO system employing linear precoding. The measurement data has been acquired using Eurecom's MIMO Openair Sounder (EMOS). The EMOS can perform realtime MIMO channel measurements synchronously over multiple users. We consider CVQ using a Fourier codebook, a random codebook and a random codebook exploiting the second order statistics of the channel. For comparison, we also show the capacity of a single-user system using time division multiple access (TDMA) with no CSIT at all. The results show that the Fourier codebook shows very poor performance in the measured channels. Random codebooks - although suboptimal - provide a much better performance in the measured channels.

[1]  Ruben De Francisco Martin Performance Optimization of MIMO Systems with Partial Channel State Information , 2008 .

[2]  Rohit U. Nabar,et al.  Introduction to Space-Time Wireless Communications , 2003 .

[3]  Robert W. Heath,et al.  Shifting the MIMO Paradigm , 2007, IEEE Signal Processing Magazine.

[4]  R. Knopp,et al.  EMOS Platform: Real-Time Capacity Estimation of MIMO Channels in the UMTS-TDD Band , 2007, 2007 4th International Symposium on Wireless Communication Systems.

[5]  Syed Ali Jafar,et al.  On the Optimality of Beamforming with Quantized Feedback , 2007, IEEE Transactions on Communications.

[6]  Shlomo Shamai,et al.  On the achievable throughput of a multiantenna Gaussian broadcast channel , 2003, IEEE Transactions on Information Theory.

[7]  Nihar Jindal MIMO broadcast channels with finite rate feedback , 2005, GLOBECOM.

[8]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.

[9]  T. Sälzer,et al.  From Single User to Multiuser Communications : Shifting the MIMO Paradigm , 2007 .

[10]  Josef A. Nossek,et al.  Multiuser MIMO Channel Measurements and Performance in a Large Office Environment , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[11]  Raymond Knopp,et al.  Correlation and capacity of measured multi-user MIMO channels , 2008, 2008 IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications.

[12]  Andrea J. Goldsmith,et al.  Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels , 2003, IEEE Trans. Inf. Theory.

[13]  Andrea J. Goldsmith,et al.  Sum power iterative water-filling for multi-antenna Gaussian broadcast channels , 2005, IEEE Transactions on Information Theory.

[14]  Tricia J. Willink,et al.  Limited Feedback Precoding in Realistic MIMO Channel Conditions , 2007, 2007 IEEE International Conference on Communications.

[15]  R. Knopp,et al.  Capacity of linear multi-user MIMO precoding schemes with measured channel data , 2008, 2008 IEEE 9th Workshop on Signal Processing Advances in Wireless Communications.

[16]  Ruben de Francisco Performance optimization of MIMO systems with partial channel state information , 2008 .

[17]  A. Lee Swindlehurst,et al.  A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization , 2005, IEEE Transactions on Communications.