GHz Spectrum Acquisition in Real-time Using the Sparse Fourier Transform

1. Haitham HassaniehMIT GHz Spectrum Acquisition in Real-time Using the Sparse Fourier Transform The rising popularity of wireless communication and the potential of a spectrum shortage have motivated the Federal Communications Committee (FCC) to take steps towards releasing multiple bands for dynamic spectrum sharing. The figure below from the Microsoft Spectrum Observatory shows that, even in urban areas, large swaths of the spectrum remain underutilized. Dynamic spectrum sharing allows us to use the spectrum more efficiently whereby wireless devices sense unoccupied frequency bands and use them. However, this requires devices to sense GHz-wide spectrum in real-time and with low power consumption. • Used sparse FFT to build a GHz receiver from three tens of MHz ADCs • Both senses and decodes the spectrum What if spectrum is not sparse? Differential BigBand Seattle January 7, 2013 (Microsoft Spectrum observatory) • Even if the spectrum is 100% occupied, changes in occupancy are sparse  Apply Sparse FFT to Changes/Diffs • Can’t subtract signals; operate over power • Real-time GHz sensing; but no decoding • Two steps: • Bucketize: Divide spectrum into a few buckets, and we can ignore empty bucket • Estimate: Estimate the large coefficient in each non-empty bucket f Problem: How to capture GHz of Spectrum? • Real-time GHz Spectrum Sensing is difficult: • Sequential scanning  miss short-lived signals • High-speed ADCs  expensive, poor resolution • Bucketize: • Subsampling time Aliasing the frequencies Differential Bigband: It can sense the spectrum even when occupancy is very high Idea: Leverage Sparsity Estimate: Repeating bucketization with time shift ∆T: • Compressive Sensing however is difficult: • Random sampling  Analog Mixers at GHz speed • Reconstruction algorithm and computing million-point FFT High power consumption • Sparse FFT: • Co-prime subsampling  Use a few low-speed ADCs • Sub-linear algorithm Computes large FFT cheaply ∆Phase Percentage of Spectrum Usage (Sparsity) • Sparse Frequencies • Buckets • Collision Resolution • Using multiple co-prime aliasing filters • Same frequencies don’t collide in two filters • Collision •  Cannot Estimate GHz Spectrum Sensing with BigBand: Result from Cambridge, MA, USA, Jan 15, 2013 MADALGO – Center for Massive Data Algorithmics, a Center of the Danish National Research Foundation • Decoding: • Senders Randomly Hopping in a GHz • Each sender sends 1MHz of BPSK signal Sparse FFT enables realtime GHz sensing and decoding for low-power portable devices Number of MHz Senders Randomly Hopping in 0.9 GHz