Adaptive thresholding for distributed power spectrum sensing

Abstract

Wideband spectrum sensing is an important prerequisite for cognitive radio access. A network sensing scenario comprising low-end sensors is considered, with each sensor reporting a single randomly filtered power measurement bit to the fusion center (FC), which estimates the ambient power spectrum from these bits. An adaptive thresholding algorithm is proposed to improve the quality and speed of power spectrum reconstruction. Upon receipt of each new bit, the FC picks the threshold for the next sensor so as to cut off a half-space from the feasible region along its Chebyshev center. Convergence of this algorithm to the true finite-length autocorrelation is shown, whose Fourier transform yields the power spectrum estimate. To avoid the `downlink' threshold communication overhead, an alternative algorithm is proposed, where each sensor pseudo-randomly chooses its threshold from a suitable distribution, and the FC judiciously polls sensors to form its power spectrum estimate.