Abstract: Aiming at the idle channel detection problem under impulsive noise environments, a new detection scheme is proposed based on Bayesian statistical reasoning framework. To cope with impulsive interference and improve probability of detection, a novel dynamic state-space model is established, in which the dynamic variations of impulsive noise and channel status are described by Bernoulli random finite sets. On the basis of above, a novel channel detection mechanism is designed based on sequential estimation and particle filtering theory. At the same time of detecting channel status, the proposed scheme jointly estimates the occurrence and amplitude of impulsive noise, therefore eliminating its interference on channel detection. Moreover, the channel detection performance can be significantly improved based on utilizing the dynamic property of impulsive noise, thus providing a promising solution for channel detection with high reliability under complex electromagnetic environments. Numerical simulations verify the effectiveness of the proposed algorithm.

Key words: idle channel detection, impulsive noise, Bayesian stochastic filter, particle filtering, joint estimation