Linear State Space Model Based Channel Estimation for High Mobility OFDM Systems

Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high data rate transmission in wireless communication and the channel estimation is very important for implementation of OFDM. Channel modeling and channel estimation in time varying channels become more challenging in high mobility communication channels. In this paper, cyclic prefix (CP) can be used as a source of channel information which is originally used to reduce inter symbol interference (ISI). The time varying channel is modeled as Complex Exponential Basis Expansion Model (CE-BEM). Based on the CP observation, we propose Linear State Space Model which reduces the complexity of Channel implementation. The proposed technique is evaluated on the basis of traditional channel model, tapped delay line model (TDL) and proposed channel model, complex exponential basis expansion model (CE-BEM).Channel estimation error MSEE is evaluated under different Doppler spreads using Cyclic Prefix (CP) and Conventional Training (CT) methods for previous and proposed channel models.