Frequency spectrum detection and unknown noise variance tracking estimation method and device thereof

Abstract

For a spectrum detection problem in the condition of dynamic unknown noise variance in the practical application, the invention discloses a dynamic state-space system model to reflect an intrinsic mechanism: an authorized user state and a time-varying noise variance are taken as two hidden states, and two-state one-order Markow and auto-regressive models are used to carry out modeling for the dynamic transfer characteristic. On this basis, a novel spectrum sensing method device is designed and provided. The method is rooted in the Bayes statistical inference theory, the marginalized particle filtering technique (in the figure) is employed, and the combined estimation of the authorized user and the noise variance can be realized. Especially, the invention discloses a marginalized particle filter two-level adaptive prediction coefficient adjustment method, the noise variance time-varying characteristic is fully utilized, and the accurate tracking of the noise variance can be realized. The obtained noise variance information is used, the algorithm device can be extended to (but not limited to) a single-node single-antenna sensing system, and good spectrum sensing performance is obtained.

Description

technical field [0001] Aiming at the problem of frequency spectrum detection under the condition of time-varying unknown noise variance, the present invention first designs and proposes a dynamic state-space system model (Dynamic State-space Model, DSM), which is based on two-state Markov state probability transition model and autoregressive (Autoregressive) model respectively. -regressive, AR) model, which regards the authorized user state and time-varying noise variance as two hidden states (Hidden States); on this basis, a novel spectrum detection method is proposed. This method is based on Bayesian theory and uses Marginal Partical Filtering (MPF) technology, which can realize accurate detection of authorized user status and real-time tracking of unknown noise variance at the same time, and significantly improve the frequency spectrum under the condition of noise variance dynamic unknown. perceived performance. belongs to the field of communication. Background technique...

AI Technical Summary

Problems solved by technology

In practical applications, due to the uncertainty and time-varying nature of noise, it is difficult for cognitive users to obtain accurate real-time noise variance, which leads to a significant decline in the detection performance of existing detection methods

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