Sub-Nyquist Wideband Spectrum Sensing and Sharing

PhDThe 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

Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

COOPERATIVE WIDEBAND SPECTRUM SENSING BASED ON JOINT SPARSITY By Ghazaleh Jowkar, Master of Science A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University Virginia Commonwealth University 2017 Major Director: Dr. Ruixin Niu, Associate Professor of Department of Electrical and Computer Engineering In this thesis, the problem of wideband spectrum sensing in cognitive radio (CR) networks using sub-Nyquist sampling and sparse signal processing techniques is investigated. To mitigate multi-path fading, it is assumed that a group of spatially dispersed SUs collaborate for wideband spectrum sensing, to determine whether or not a channel is occupied by a primary user (PU). Due to the underutilization of the spectrum by the PUs, the spectrum matrix has only a small number of non-zero rows. In existing state-of-the-art approaches, the spectrum sensing problem was solved using the low-rank matrix completion technique involving matrix nuclear-norm minimization. Motivated by the fact that the spectrum matrix is not only low-rank, but also sparse, a spectrum sensing approach is proposed based on minimizing a mixed-norm of the spectrum matrix instead of low-rank matrix completion to promote the joint sparsity among the column vectors of the spectrum matrix. Simulation results are obtained, which demonstrate that the proposed mixed-norm minimization approach outperforms the low-rank matrix completion based approach, in terms of the PU detection performance. Further we used mixed-norm minimization model in multi time frame detection. Simulation results shows that increasing the number of time frames will increase the detection performance, however, by increasing the number of time frames after a number of times the performance decrease dramatically

Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks

Cognitive radio has been widely considered as one of the prominent solutions to tackle the spectrum scarcity. While the majority of existing research has focused on single-band cognitive radio, multiband cognitive radio represents great promises towards implementing efficient cognitive networks compared to single-based networks. Multiband cognitive radio networks (MB-CRNs) are expected to significantly enhance the network's throughput and provide better channel maintenance by reducing handoff frequency. Nevertheless, the wideband front-end and the multiband spectrum access impose a number of challenges yet to overcome. This paper provides an in-depth analysis on the recent advancements in multiband spectrum sensing techniques, their limitations, and possible future directions to improve them. We study cooperative communications for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also investigate several limits and tradeoffs of various design parameters for MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE Journal, Special Issue on Future Radio Spectrum Access, March 201