Abstract
This correspondence proposes a new technique for signal classification and jamming detection in wide-band (WB) radios. Theory of compressed sensing is exploited to recover the sparsely populated WB spectrum from sub-Nyquist samples, thus reducing the very high-rate sampling requirements at the receiver analog to digital converter. From the recovered WB, key spectral features of each narrow-band (NB) signal are extracted. These spectral features are then used to train a simple yet powerful classifier, the naïve Bayes classifier (NBC). The trained NBC is then used not only to classify various NB signals into their respective modulations but also to detect the jamming on different NB signals, which are the main contributions of this letter. The proposed algorithm is then evaluated under different empirical setups and is shown to perform better when compared to a recently proposed feature-based jamming detection algorithm.
| Original language | English |
|---|---|
| Pages (from-to) | 1398-1401 |
| Number of pages | 4 |
| Journal | IEEE Communications Letters |
| Volume | 22 |
| Issue number | 7 |
| DOIs | |
| State | Published - 2018 Jul 1 |
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Keywords
- Wide-band radios
- compressed sensing
- jamming detection
- naïve Bayes classifier
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Signal classification and jamming detection in wide-band radios using naïve bayes classifier. / Mughal, M. O.; Kim, Sunwoo.
In: IEEE Communications Letters, Vol. 22, No. 7, 01.07.2018, p. 1398-1401.Research output: Contribution to journal › Article
TY - JOUR
T1 - Signal classification and jamming detection in wide-band radios using naïve bayes classifier
AU - Mughal, M. O.
AU - Kim, Sunwoo
PY - 2018/7/1
Y1 - 2018/7/1
N2 - This correspondence proposes a new technique for signal classification and jamming detection in wide-band (WB) radios. Theory of compressed sensing is exploited to recover the sparsely populated WB spectrum from sub-Nyquist samples, thus reducing the very high-rate sampling requirements at the receiver analog to digital converter. From the recovered WB, key spectral features of each narrow-band (NB) signal are extracted. These spectral features are then used to train a simple yet powerful classifier, the naïve Bayes classifier (NBC). The trained NBC is then used not only to classify various NB signals into their respective modulations but also to detect the jamming on different NB signals, which are the main contributions of this letter. The proposed algorithm is then evaluated under different empirical setups and is shown to perform better when compared to a recently proposed feature-based jamming detection algorithm.
AB - This correspondence proposes a new technique for signal classification and jamming detection in wide-band (WB) radios. Theory of compressed sensing is exploited to recover the sparsely populated WB spectrum from sub-Nyquist samples, thus reducing the very high-rate sampling requirements at the receiver analog to digital converter. From the recovered WB, key spectral features of each narrow-band (NB) signal are extracted. These spectral features are then used to train a simple yet powerful classifier, the naïve Bayes classifier (NBC). The trained NBC is then used not only to classify various NB signals into their respective modulations but also to detect the jamming on different NB signals, which are the main contributions of this letter. The proposed algorithm is then evaluated under different empirical setups and is shown to perform better when compared to a recently proposed feature-based jamming detection algorithm.
KW - Wide-band radios
KW - compressed sensing
KW - jamming detection
KW - naïve Bayes classifier
UR - http://www.scopus.com/inward/record.url?scp=85046343698&partnerID=8YFLogxK
U2 - 10.1109/LCOMM.2018.2830769
DO - 10.1109/LCOMM.2018.2830769
M3 - Article
AN - SCOPUS:85046343698
VL - 22
SP - 1398
EP - 1401
JO - IEEE Communications Letters
JF - IEEE Communications Letters
SN - 1089-7798
IS - 7
ER -