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Dynamic Sampling Rate Adjustment for Compressive Spectrum Sensing over Cognitive Radio Network
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Ching-Chun Huang and Li-Chun Wang
Our system model for cooperative compressive spectrum sensing with dynamic sampling rate adjustment.
The dynamic system for sampling rate tracking.
Abstract
In this paper, a dynamic sampling rate adjustment scheme is proposed for compressive spectrum sensing in cognitive radio network. Nowadays, compressive sensing (CS) has been proposed with a revolutionary idea to sense the sparse spectrum by using a lower sampling rate. However, many methods for compressive spectrum sensing assume that the sparse level is static and a fixed compressive sampling rate is applied over time. To adapt to time-varying sparse levels and adjust the sampling rate, we proposed to model sparse levels as a dynamic system and treat the dynamic rate selection as a tracking problem. By introducing the Sequential Monte Carlo (SMC) algorithm into a distributed compressive spectrum sensing framework, we could not only track the optimal sampling rate but determine the unoccupied channels accurately in a unified method.
Estimation flow of posterior distribution based on SMC.
The proposed dynamic rate adjustment scheme over a cognitive radio network with time-varying sparse levels. Sensing index (K) 2 [1; 40].
SMC-based tracking concept with a distributed compressive spectrum sensing framework, the optimal sampling rate is well-tracked and the free channels are well-determined.
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