Capacity of Time-Varying Rayleigh Fading MIMO Channels

In this paper, we investigate the capacity of continuously time-varying multiple-input multiple-output (MIMO) systems in frequency-flat Rayleigh fading environment with perfect interleaving. By introducing the Gauss-Markov model to describe the channel variation and employing minimum mean square error (MMSE) channel estimation based on the pilots, we derive very tight lower and upper bounds of the ergodic capacity in closed-form. We also derive the optimal power allocation between the pilot and data vectors, which maximize the lower bound of the ergodic capacity. Interestingly, the optimal allocation is independent of the channel variation parameter and can be easily computed (no feedback is required). The optimal training interval can be obtained via numerical optimization. Finally, two different transmit schemes are compared via simulation. It is shown that, in fast fading or high SNR environments, the scheme with optimal power allocation almost has the same performance as the scheme with equal power allocation. However, in slowly fading or low SNR environments, the former has much better performance than the latter

[1]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[2]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

[3]  Josef A. Nossek,et al.  Fading correlations in wireless MIMO communication systems , 2003, IEEE J. Sel. Areas Commun..

[4]  Muriel Médard,et al.  The effect upon channel capacity in wireless communications of perfect and imperfect knowledge of the channel , 2000, IEEE Trans. Inf. Theory.

[5]  Andrea J. Goldsmith,et al.  Capacity of fading MIMO channels with channel estimation error , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[6]  王京,et al.  Analytical Expression for the MIMO Channel Capacity , 2006 .

[7]  Yifei Zhao,et al.  Analytical Expression for the MIMO Channel Capacity , 2006 .

[8]  Babak Hassibi,et al.  How much training is needed in multiple-antenna wireless links? , 2003, IEEE Trans. Inf. Theory.

[9]  Andrea J. Goldsmith,et al.  Transmitter optimization and optimality of beamforming for multiple antenna systems , 2004, IEEE Transactions on Wireless Communications.

[10]  Milica Stojanovic,et al.  Analysis of the impact of channel estimation errors on the performance of a decision-feedback equalizer in fading multipath channels , 1995, IEEE Trans. Commun..

[11]  Chen-Nee Chuah,et al.  Capacity scaling in MIMO Wireless systems under correlated fading , 2002, IEEE Trans. Inf. Theory.

[12]  A. Lee Swindlehurst,et al.  Capacity-optimal training for space-time modulation over a time-varying channel , 2003, IEEE International Conference on Communications, 2003. ICC '03..

[13]  Holger Boche,et al.  Channel capacity and capacity-range of beamforming in MIMO wireless systems under correlated fading with covariance feedback , 2004, IEEE Transactions on Wireless Communications.

[14]  Mansoor Shafi,et al.  Capacity of MIMO systems with semicorrelated flat fading , 2003, IEEE Trans. Inf. Theory.

[15]  Ananthram Swami,et al.  Optimal training over the Gauss-Markov fading channel: a cutoff rate analysis , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[16]  Donald C. Cox,et al.  Channel and capacity estimation errors , 2002, IEEE Communications Letters.

[17]  David R. Cox,et al.  Correlation Bandwidth and Delay Spread Multipath Propagation Statistics for 910-MHz Urban Mobile Radio Channels , 1975, IEEE Trans. Commun..

[18]  Andrea J. Goldsmith,et al.  MIMO capacity with channel uncertainty: does feedback help? , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[19]  B. Nelin,et al.  Corrections to "The Effect of Frequency Selective Fading on the Binary Error Probabilities of Incoherent and Differentially Coherent Matched Filter Receivers" , 1963 .