چکیده (انگلیسی):

This study proposes an efficient channel-estimation scheme for Multiband (MB) Orthogonal Frequency Division
Multiplexing (OFDM)-based Ultra Wide Band (UWB) communication systems. One of the challenges in wireless
system is the frequency selective fading caused due to multipath channel between the transmitter and receiver. The
signal bandwidth in broad band cellular wireless systems typically exceeds the coherence bandwidth of the
multipath channel. To overcome such a multipath fading environment with low complexity and to increase the
performance, UWB OFDM system is used. To practically realize MB-OFDM UWB, one needs to cope with
numerous design challenges, particularly in receiver designs such as symbol timing, Carrier Frequency Offset
(CFO) and sampling frequency offset compensation, as well as Channel Frequency Response (CFR) estimation.
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 study. 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, simulation results are given and found that it gives more accurate prediction of channel coefficients as
compared with fuzzy channel estimator under various highly noisy multipath channel conditions.