An Intelligent Algorithm for Channel Estimation of UWB OFDM Communication

Orthogonal Frequency Division Multiplexing (OFDM) has recently been applied in wireless communication systems due to its high data rate transmission capability with high bandwidth efficiency and its robustness to multi-path delay. UWB (Ultra Wide Band) OFDM communication was proposed for physical layer in the IEEE 802.15.3a standard which covers wideband communication in wireless personal area networks. Channel estimation is an important issue for wireless communication systems. Dynamic channel estimation is necessary before demodulation of UWB OFDM signals since the radio channel is time varying and frequency selective for wideband systems. A channel estimation scheme using a Takagi-Sugeno (T-S) fuzzy-based neural network under the time varying velocity of the mobile station in a UWB OFDM system is proposed in this paper. In our proposal, by utilizing the learning capability of adaptive neuro-fuzzy inference system (ANFIS), the ANFIS is trained with correct channel state information then the trained network is used as a channel estimator. To validate the performance of our proposed method, several simulation results are given and compared with Least Squares (LS) method. The estimated coefficients have been tracked using T-S fuzzy-based neural network and found that it gives more accurate prediction of channel coefficients as compared with Least Squares (LS) channel estimation technique.