Name
Abstract | ||
|---|---|---|
A parallel structure to do spectrum sensing in cognitive radio (CR) at sub-Nyquist rate is proposed. The structure is based on compressed sensing (CS) that exploits the sparsity of frequency utilization. Specifically, the received analog signal is segmented or time-windowed and CS is applied to each segment independently using an analog implementation of the inner product, then all the samples are processed together to reconstruct the signal. Applying the CS framework to the analog signal directly relaxes the requirements in wideband RF receiver front-ends. Moreover, the parallel structure provides a design flexibility and scalability on the sensing rate and system complexity. This paper also provides a joint reconstruction algorithm that optimally detects the information symbols from the sub-Nyquist analog projection coefficients. Simulations showing the efficiency of the proposed approach are also presented. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICASSP.2008.4518496 | ICASSP |
Keywords | Field | DocType |
frequency utilization,sub-nyquist,segmented compressed sensing,cognitive radio,subnyquist analog projection coefficients,mixed-signal reception,parallel sensing,index terms— cognitive radio,radiofrequency receivers,compressed sensing,parallel structure,radio reception,signal reconstruction,signal segmentation,spectrum sensing,wideband receiver,parallel,mixed-signal sensing,algorithms,broadband,signal processing,inner product,analog signals,compression,radiofrequency,spectra,frequency division multiplexing,front end,indexing terms | Signal processing,Telecommunications,Computer science,Frequency-division multiplexing,Electronic engineering,Artificial intelligence,Analog signal,Compressed sensing,Wideband,Pattern recognition,Mixed-signal integrated circuit,Signal reconstruction,Cognitive radio | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4244-1484-0 | 978-1-4244-1484-0 | 60 |
PageRank | References | Authors |
2.92 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuizhuan Yu | 1 | 145 | 11.51 |
| Sebastian Hoyos | 2 | 234 | 29.24 |
| Brian M. Sadler | 3 | 3179 | 286.72 |