On superimposed-training power allocation for time-varying channel estimation

Channel estimation for single-input single-output (SISO) frequency-selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be described by an orthogonal polynomial basis expansion model (OP-BEM). A periodic (non-random) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. First we present a novel approach to channel estimation using only the first-order statistics of the data under a fixed power allocation to training. We then present a performance analysis of this approach for time-varying random channels to obtain a closed-form expression for the channel estimation variance. Finally, we address the issue of superimposed training power allocation. Illustrative computer simulation examples are presented where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' model