Reducing the Analog and Digital Bandwidth Requirements of RF Receivers for Measuring Periodic Sparse Waveforms

• Published in: IEEE transactions on instrumentation and measurement 2012-11, Vol.61 (11), p.2960-2971
• Main Authors: Nader, Charles, Van Moer, W., Bjorsell, N., Barbe, K., Handel, P.
• Format: Article
• Language: English
• Subjects: Analog-digital conversion; Channelization; ell_{1} -norm minimization; Frequency measurement; harmonic sampling; l-norm minimization; Radio frequency; radio-frequency (RF) measurement systems; Reconstruction algorithms; signal separation; Source separation; sparse signals; spectrum reconstruction; Wideband
• ISSN: 0018-9456; 1557-9662; 1557-9662
• DOI: 10.1109/TIM.2012.2203729

In this paper, a prototype setup for measuring wideband periodic waveforms whose bandwidth surpasses the analog bandwidth of a radio-frequency receiver is presented. Three major challenges arise in the analog-to-digital stage when measuring such wideband waveforms: the availability of a high sampling rate based on a good amplitude resolution; the availability of the required analog bandwidth to capture the full waveform; and achieving the previous requirements in a cheap way. Those challenges are more pronounced when using wideband modulated signals to test nonlinear devices and when measuring/sensing wideband spectra for cognitive radio applications. For periodic signals, undersampling techniques based on the evolved harmonic sampling can be used to reduce the sampling rate requirements while satisfying a good amplitude resolution. For sparse signals, a technique based on channelization and signal separation is proposed. This technique splits the spectrum of the waveform into parallel channels, downconverts them to the analog frequency band of the analog-to-digital converter (ADC), spreads the channel information, sums them, and then digitizes with a single ADC. Using reconstruction algorithms based on l 1 -norm minimization, the information of the parallel channels can be separated. The original wideband spectrum can be then reconstructed after de-embedding of the channelization process.