This paper considers multiple hypothesis techniques for the identification of significant channel parameters in OFDM systems. Unlike several OFDM channel estimation methods, where the channel response is estimated based on a pre-defined or an estimated length. In this work, the channel length is assumed unknown and only significant channel parameters will be identified. First, using a training based scenario, a model is proposed for the channel response. Second the model parameters are estimated using least squares (LS). Based on those estimates, multiple hypothesis tests based on F-statistics are constructed to classify each significant parameter. Simulation results show that the method is capable of identifying significant channel parameters with high probability under various SNR. In addition a receiver based on those parameters have a better performance than one which is based on the full model.
展开▼
