In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use par...In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.展开更多
基金Project supported by the National Fundamental Research (Grant Nos.2009CB3020402,2010CB731803)the National Natural Science Foundation of China (Grant Nos.60702046,60832005,60972050,60632040)the Natural High-Technology Research and Development Program of China (Grant Nos.2007AA01Z267,2009AA01Z248,2009AA011802)
文摘In this paper,a distributed compressive spectrum sensing scheme in wideband cognitive radio networks is investigated.An analog-to-information converters（AIC） RF front-end sampling structure is proposed which use parallel low rate analog to digital conversions（ADCs） and fewer storage units for wideband spectrum signal sampling.The proposed scheme uses multiple low rate congitive radios（CRs） collecting compressed samples through AICs distritbutedly and recover the signal spectrum jointly.A general joint sparsity model is defined in this scenario,along with a universal recovery algorithm based on simultaneous orthogonal matching pursuit（S-OMP）.Numerical simulations show this algorithm outperforms current existing algorithms under this model and works competently under other existing models.
