Sub-Nyquist rate wideband spectrum sensing over TV white space for M2M communications

Abstract

Secondary operation in TV White Space (TVWS) calls for fast and accurate spectrum sensing over a wide bandwidth, which challenges the traditional spectrum sensing methods operating at or above Nyquist rate. Sub-Nyquist sampling has attracted significant interests for wideband spectrum sensing, while existing algorithms can only work for sparse spectrum with high computation and hardware complexity. In this paper, we propose a novel sub-Nyquist wideband sensing algorithm that can work for the non-sparse spectrum without sampling at full bandwidth through the use of multiple low-speed Analog-to-Digital Converters (ADCs) based on sparse Fast Fourier Transform (sFFT). The proposed permutation and filtering algorithm achieves the wideband sensing regardless of signal sparsity with low hardware complexity. In contrast to existing sub-Nyquist approaches, the proposed wideband sensing algorithm subsamples the wideband signal, and then directly estimates its frequency spectrum. The mathematical model of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis over TVWS signals. The proposed algorithm shows considerable detection performance on wideband signals as well as reduces the runtime and implementation complexity in comparison with conventional wideband sensing algorithm.