Optimal placement of training for frequency-selective block-fading channels

The problem of placing training symbols optimally for orthogonal frequency-division multiplexing (OFDM) and single-carrier systems is considered. The channel is assumed to be quasi-static with a finite impulse response of length (L + 1) samples. Under the assumptions that neither the transmitter nor the receiver knows the channel, and that the receiver forms a minimum mean square error (MMSE) channel estimate based on training symbols only, training is optimized by maximizing a tight lower bound on the ergodic training-based independent and identically distributed (i.i.d.) capacity. For OFDM systems, it is shown that the lower bound is maximized by placing the known symbols periodically in frequency. For single-carrier systems, under the assumption that the training symbols are placed in clusters of length /spl alpha/ /spl ges/ (2L + 1), it is shown that the lower bound is maximized by a family of placement schemes called QPP-/spl alpha/, where QPP stands for quasi-periodic placement. These placement schemes are formed by grouping the known symbols into as many clusters as possible and then placing these clusters periodically in the packet. For both OFDM and single-carrier systems, the optimum energy tradeoff between training and data is also obtained.

[1]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[2]  Lang Tong,et al.  Optimal Placement of Known Symbols for Nonergodic Broadcast Channels , 2002 .

[3]  Shlomo Shamai,et al.  The intersymbol interference channel: lower bounds on capacity and channel precoding loss , 1996, IEEE Trans. Inf. Theory.

[4]  Shlomo Shamai,et al.  Fading channels: How perfect need "Perfect side information" be? , 2002, IEEE Trans. Inf. Theory.

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

[6]  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.

[7]  S. Shamai,et al.  The capacity of discrete-time Rayleigh fading channels , 1997, Proceedings of IEEE International Symposium on Information Theory.

[8]  Thomas Kailath,et al.  Optimal training for frequency-selective fading channels , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[9]  Georgios B. Giannakis,et al.  Optimal training and redundant precoding for block transmissions with application to wireless OFDM , 2002, IEEE Trans. Commun..

[10]  Lang Tong,et al.  Detection with embedded known symbols: optimal symbol placement and equalization , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[11]  R. Negi,et al.  Pilot Tone Selection For Channel Estimation In A Mobile Ofdm System * , 1998, International 1998 Conference on Consumer Electronics.

[12]  Thomas L. Marzetta,et al.  Capacity of a Mobile Multiple-Antenna Communication Link in Rayleigh Flat Fading , 1999, IEEE Trans. Inf. Theory.

[13]  Shlomo Shamai,et al.  Fading Channels: Information-Theoretic and Communication Aspects , 1998, IEEE Trans. Inf. Theory.

[14]  G. David Forney,et al.  Modulation and Coding for Linear Gaussian Channels , 1998, IEEE Trans. Inf. Theory.