Compressive Wideband Spectrum Sensing and Signal Recovery With Unknown Multipath Channels

Hongwei Wang, Jun Fang, Huiping Duan, Hongbin Li

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

We study the problem of joint wideband spectrum sensing and recovery of multi-band signals in a multi-antenna-based sub-Nyquist sampling framework. Specifically, the multi-band signal is composed of a number of uncorrelated narrowband signals spreading over a wide frequency band. Unlike existing works which assume the source signals impinge on the receiver via a line-of-sight (LOS) path, we consider a more practical unknown MIMO channel which results from multipath propagation. A new sub-Nyquist sampling architecture is proposed, where each antenna output passes through two channels, namely, a direct path and a delayed path with a controlled amount of time delay. The signal at each channel is then sampled by a synchronized low-rate analog-to-digital converter (ADC). We utilize the collected data samples to build a set of cross-correlation matrices with different time lags and develop a CANDECOMP/PARAFAC (CP) decomposition-based method to recover the carrier frequencies, power spectra as well as the source signals themselves. Recovery conditions of the proposed method are analyzed, and Cramér-Rao bound (CRB) results for our estimation problem are derived. Simulation results are presented to illustrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)5305-5316
Number of pages12
JournalIEEE Transactions on Wireless Communications
Volume21
Issue number7
DOIs
StatePublished - 1 Jul 2022

Keywords

  • CANDECOMP/PARAFAC decomposition
  • Wideband spectrum sensing
  • multipath propagation
  • sub-Nyquist sampling

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