  The growing scarcity of spectrum resources, wideband spectrum sensing is
required to process a prohibitive volume of data at a high sampling rate. For
some applications, spectrum estimation only requires second-order statistics.
In this case, a fast power spectrum sensing solution is proposed based on the
generalized coprime sampling. By exploring the sensing vector inherent
structure, the autocorrelation sequence of inputs can be reconstructed from
sub-Nyquist samples by only utilizing the parallel Fourier transform and simple
multiplication operations. Thus, it takes less time than the state-of-the-art
methods while maintaining the same performance, and it achieves higher
performance than the existing methods within the same execution time, without
the need for pre-estimating the number of inputs. Furthermore, the influence of
the model mismatch has only a minor impact on the estimation performance, which
allows for more efficient use of the spectrum resource in a distributed swarm
scenario. Simulation results demonstrate the low complexity in sampling and
computation, making it a more practical solution for real-time and distributed
wideband spectrum sensing applications.
