Application of the evidence procedure to the estimation of the number of paths in wireless channels

This paper addresses application of the Bayesian evidence procedure to the analysis of wireless channels. We use relevance vector machines, a kernel-based technique to locally maximize evidence that turns out to be promising in the context of the wireless channel estimation. This approach not only allows to estimate channel parameters, but also provides a tool to assess the number of multipath components. We show that in the case of channel sounding using a pulse-compression technique, it is possible to design an optimal kernel, as well as to estimate parameters of the additive noise and base on it a thresholding level to implement model order estimation. The applicability of the proposed scheme is demonstrated with synthetic as well as real channel measurements.