Cognitive channel classification and parameter estimation technique for UWB through-wall propagation channel modeling

In this paper, a cognitive classification and parameter estimation technique for the UWB through-wall propagation channel is proposed. For an investigated channel, a closed-loop cognitive radio system is proposed and the membership degree of each hypothesis channel is obtained for the channel classification and parameter estimation. The system contains an adaptive wave design technique in the transmitter, condition of convergence in the receiver and Bayesian updating in the feedback. For an instance to verify the channel classification and parameter estimation technique, the Saleh-Valenzuela model and its key parameters are selected. We focus on 802.15 CM1-CM4 channels and build their impulse response sets as the hypothesis channel sets. The following Monte Carlo simulations demonstate the efficiency, accuracy and reliability of the proposed approach, even in low SNR.