Abstract: Methods and apparatus for monitoring wideband GHz spectrum for wireless communication, and sensing and decoding respective frequency components of a time-varying signal corresponding to the monitored spectrum. Concepts relating to sparse Fast Fourier Transform (sFFT) techniques facilitate identification of one or more frequency components of a sparsely occupied spectrum by sub-sampling the signal corresponding to the monitored spectrum at a sampling rate below the Nyquist criterion. The disclosed methods and apparatus may be implemented using conventional relatively low-power wireless receivers and using off-the-shelf relatively inexpensive low-speed and low-power analog-to-digital converters (ADCs) typically employed in WiFi devices or cellular phones, in tandem with unique processing techniques based on sFFTs and sub-Nyquist criterion sampling, and have demonstrated efficacy even in scenarios where the monitored spectrum is not sparse.

Abstract: Methods and apparatus for monitoring wideband GHz spectrum for wireless communication, and sensing and decoding respective frequency components of a time-varying signal corresponding to the monitored spectrum. Concepts relating to sparse Fast Fourier Transform (sFFT) techniques facilitate identification of one or more frequency components of a sparsely occupied spectrum by sub-sampling the signal corresponding to the monitored spectrum at a sampling rate below the Nyquist criterion. The disclosed methods and apparatus may be implemented using conventional relatively low-power wireless receivers and using off-the-shelf relatively inexpensive low-speed and low-power analog-to-digital converters (ADCs) typically employed in WiFi devices or cellular phones, in tandem with unique processing techniques based on sFFTs and sub-Nyquist criterion sampling, and have demonstrated efficacy even in scenarios where the monitored spectrum is not sparse.

Abstract: An ASIC for monitoring wideband GHz spectrum to sense respective frequency components present in the spectrum. The ASIC implements Fast Fourier Transform (FFT) techniques to facilitate identification of one or more frequency components of a sparse signal after the signal is sub-sampled at a rate below the Nyquist criterion. The ASIC computes a first Fast Fourier Transform (FFT) of a first sub-sampled set of samples of a time-varying signal representing the monitored spectrum and sampled at a first sampling rate, and further computes a second FFT of a second sub-sampled set of samples of the time-varying signal sampled at a second sampling rate different from the first sampling rate. In one example, each of the first FFT and the second FFT is a low-radix FFT to facilitate a low-power and low-cost ASIC implementation of wideband spectrum sensing.

Abstract: An ASIC for monitoring wideband GHz spectrum to sense respective frequency components present in the spectrum. The ASIC implements Fast Fourier Transform (FFT) techniques to facilitate identification of one or more frequency components of a sparse signal after the signal is sub-sampled at a rate below the Nyquist criterion. The ASIC computes a first Fast Fourier Transform (FFT) of a first sub-sampled set of samples of a time-varying signal representing the monitored spectrum and sampled at a first sampling rate, and further computes a second FFT of a second sub-sampled set of samples of the time-varying signal sampled at a second sampling rate different from the first sampling rate. In one example, each of the first FFT and the second FFT is a low-radix FFT to facilitate a low-power and low-cost ASIC implementation of wideband spectrum sensing.