Sub-Nyquist Wideband Spectrum Sensing and Sharing
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The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate e cient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implemen- tation challenge in compact commodity radios that have limited energy and computation capabilities. The sampling rates speci ed by the Shannon-Nyquist theorem impose great challenges both on the acquisition hardware and the subsequent storage and digital sig- nal processors. Sub-Nyquist sampling was thus motivated to sample wideband signals at rates far lower than the Nyquist rate, while still retaining the essential information in the underlying signals. This thesis proposes several algorithms for invoking sub-Nyquist sampling in wideband spectrum sensing. Speci cally, a sub-Nyquist wideband spectrum sensing algorithm is proposed that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters based on sparse Fast Fourier Transform. To lower signal spectrum sparsity while maintaining the channel state information, the received signal is pre-processed through a proposed permutation and ltering algorithm. Additionally, a low-complexity sub-Nyquist wideband spectrum sensing scheme is proposed that locates occupied channels blindly by recovering the sig- nal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users, an e cient coopera- tive spectrum sensing scheme is developed, in which the energy consumption on signal acquisition, processing, and transmission is reduced with the detection performance guar- antee. To further reduce the computation complexity of wideband spectrum sensing, a hybrid framework of sub-Nyquist wideband spectrum sensing with geolocation database is explored. Prior channel information from geolocation database is utilized in the sens- ing process to reduce the processing requirements on the sensor nodes. The models of the proposed algorithms are derived and veri ed by numerical analyses and tested on both real-world and simulated TV white space signals.
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