Dynamic Sampling Rate Adjustment for Compressive Spectrum Sensing over Cognitive Radio Network

作者：Huang Ching Chun^*; Wang Li Chun

来源：IEEE Wireless Communications Letters, 2012, 1(2): 57-60.

DOI：10.1109/WCL.2012.010912.110136

• Cognitive radio
• dynamic system
• sequential Monte Carlo
• compressive spectrum sensing

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.

• 出版日期2012-4