Title :
Performance analysis and training power allocation for channel estimation using superimposed training
Author :
Tugnait, Jitendra K. ; Meng, Xiaohong
Author_Institution :
Auburn Univ., AL, USA
Abstract :
Channel estimation for single-input multiple-output (SIMO) time-invariant channels using superimposed training has been considered recently by several authors. In particular, J.K. Tugnait and Weilin Luo (see IEEE Commun. Lett., vol.CL-8, p.413-15, 2003) proposed channel estimation using only the first-order statistics of the data under a fixed power allocation for training. We first present a performance analysis of the approach of Tugnait and Luo to obtain a closed-form expression for the channel estimation variance. We then address the issue of superimposed training power allocation for complex Gaussian random (Rayleigh) channels. Using the developed channel estimation variance expression, we cast the power allocation problem as one of optimizing a signal-to-noise ratio (SNR) for equalizer design. Illustrative simulation examples are provided.
Keywords :
Gaussian channels; Rayleigh channels; channel estimation; equalisers; learning (artificial intelligence); optimisation; radio links; statistical analysis; Gaussian channels; Rayleigh channels; SNR optimization; channel estimation variance; closed-form expression; complex Gaussian random channels; equalizer design; first-order statistics; signal-to-noise ratio optimization; single-input multiple-output channels; superimposed training; time-invariant channels; training power allocation; Channel estimation; Closed-form solution; Design optimization; Equalizers; Performance analysis; Power engineering and energy; Power engineering computing; Signal design; Signal to noise ratio; Statistics;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on
Print_ISBN :
0-7803-8874-7
DOI :
10.1109/ICASSP.2005.1415745