Sinusoidal Modeling and Adaptive Channel Prediction in Mobile OFDM Systems
We propose a wireless fading channel prediction algorithm for a pilot-symbol aided orthogonal frequency division multiplexing (OFDM) system. Assuming a doubly selective (time and frequency varying) ray-based physical channel model and equispaced pilot subcarriers in time and frequency, this algorithm performs channel model parameter acquisition using a 2-Step 1-D ESPRIT (estimation of signal parameters via rotational invariance techniques) as a first stage, and channel prediction via model extrapolation as a second stage. Since the channel model parameter acquisition has cubic complexity, we also propose a linear complexity channel parameter tracking algorithm based on an improved adaptive ESPRIT algorithm to continuously adapt to the time-varying channel model parameters. We derive the Cramer-Rao lower bound (CRLB) and asymptotic CRLB (ACRLB) for the mean squared error (MSE) in OFDM channel prediction. We show that our proposed OFDM channel prediction algorithm has better MSE performance while maintaining similar computational complexity than previous methods in sparse multipath fading channels characterized by specular scattering, which is most suitable for outdoor mobile macrocell scenarios. Thus, our method can be seen as a complement to the existing schemes that are more suitable for dense multipath channels with diffuse scattering, which is typical of urban pico-cell and indoor wireless scenarios. We provide simulation results based on the IEEE 802.16e mobile broadband wireless access standard to corroborate our claims.