Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio
Abstract
:1. Introduction
- Classify received licit signals into their corresponding modulation schemes using CFD and artificial neural network;
- Detect both multi-tone and modulated pulsed stealthy jammers using the CFD and the same trained artificial neural network classifier as above.
2. System Model and Problem Formulation
- Jammer is equipped with ED sensing technique and uses multi-tone as the jamming strategy to jam multiple NB signals in the observed WB signal. A tone with sufficiently higher power than the licit signal can jam any of the occupied SBs as shown in Figure 1.
- Jammer is equipped with a feature detector; hence, it is able to recognise the modulation schemes of transmitted signals and, therefore, uses the optimal pulsed (modulated) jamming schemes against the target signals, as shown in Figure 1.
- -
- No jamming: jammer is not transmitting
- -
- Tone jamming: jammer employs multi-tone to jam the NB signals in WB spectrum
- -
- Pulsed jamming: jammer employs pulsed jamming to jam the NB signals in WB spectrum. We used the MatLab environment to simulate the system model according to the specifications provided above.
3. Proposed Algorithm
3.1. Cyclostationary Spectral Analysis
3.2. Artificial Neural Network and Proposed Algorithm
Algorithm 1 Pseudo-code for proposed algorithm |
1: function Joint Signal Classification and Stealthy Jammer Detection 2: Input: 3: Train → Train ANN with Labelled data set 4: Test → Independent data set 5: Output: 6: Predicted → Signal class 7: Procedure: 8: Initialise all SB states to “free” 9: Receive the WB signal 10: Divide WB into j SBs 11: for , do 12: Compute the SCF of each NB signal 13: Obtain the and f-profiles from SCF 14: Feed the concatenated and f frequency profiles for to previously trained ANN 15: Decision ← Signal class 16: end for 17: end function |
4. Simulation Results and Discussion
4.1. Dedicated ANN Architecture for the Stealthy Jamming Attacks
4.2. A Single ANN Architecture for Both Stealthy Jamming Attacks
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Signal Class | BPSK | QPSK | BPSK-TJammed | QPSK-TJammed |
---|---|---|---|---|
BPSK | 977 | 21 | 2 | 0 |
QPSK | 33 | 967 | 0 | 0 |
BPSK-TJammed | 2 | 3 | 987 | 8 |
QPSK-TJammed | 0 | 0 | 8 | 992 |
Signal Class | BPSK | QPSK | BPSK-TJammed | QPSK-TJammed |
---|---|---|---|---|
BPSK | 990 | 10 | 0 | 0 |
QPSK | 13 | 985 | 0 | 3 |
BPSK-TJammed | 0 | 0 | 992 | 8 |
QPSK-TJammed | 0 | 0 | 2 | 998 |
Signal Class | BPSK | QPSK | BPSK-TJammed | QPSK-TJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-TJammed | 0 | 0 | 1000 | 0 |
QPSK-TJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-TJammed | QPSK-TJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-TJammed | 0 | 0 | 1000 | 0 |
QPSK-TJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-TJammed | QPSK-TJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-TJammed | 0 | 0 | 1000 | 0 |
QPSK-TJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-TJammed | QPSK-TJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-TJammed | 0 | 0 | 1000 | 0 |
QPSK-TJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-PJammed | QPSK-PJammed |
---|---|---|---|---|
BPSK | 990 | 5 | 3 | 2 |
QPSK | 3 | 988 | 2 | 7 |
BPSK-PJammed | 0 | 0 | 992 | 8 |
QPSK-PJammed | 0 | 0 | 5 | 995 |
Signal Class | BPSK | QPSK | BPSK-PJammed | QPSK-PJammed |
---|---|---|---|---|
BPSK | 990 | 10 | 0 | 0 |
QPSK | 13 | 985 | 0 | 3 |
BPSK-PJammed | 0 | 0 | 992 | 8 |
QPSK-PJammed | 0 | 0 | 2 | 998 |
Signal Class | BPSK | QPSK | BPSK-PJammed | QPSK-PJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-PJammed | 0 | 0 | 1000 | 0 |
QPSK-PJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-PJammed | QPSK-PJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-PJammed | 0 | 0 | 1000 | 0 |
QPSK-PJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-PJammed | QPSK-PJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-PJammed | 0 | 0 | 1000 | 0 |
QPSK-PJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-PJammed | QPSK-PJammed |
---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 |
BPSK-PJammed | 0 | 0 | 1000 | 0 |
QPSK-PJammed | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-PJ | QPSK-PJ | BPSK-TJ | QPSK-TJ |
---|---|---|---|---|---|---|
BPSK | 968 | 23 | 9 | 0 | 0 | 0 |
QPSK | 2 | 914 | 15 | 1 | 1 | 67 |
BPSK-PJ | 9 | 7 | 907 | 1 | 71 | 5 |
QPSK-PJ | 0 | 0 | 2 | 954 | 1 | 43 |
BPSK-TJ | 0 | 0 | 48 | 5 | 933 | 14 |
QPSK-TJ | 0 | 3 | 0 | 23 | 13 | 961 |
Signal Class | BPSK | QPSK | BPSK-PJ | QPSK-PJ | BPSK-TJ | QPSK-TJ |
---|---|---|---|---|---|---|
BPSK | 980 | 20 | 0 | 0 | 0 | 0 |
QPSK | 13 | 984 | 3 | 0 | 0 | 0 |
BPSK-PJ | 0 | 1 | 982 | 10 | 7 | 0 |
QPSK-PJ | 0 | 0 | 0 | 985 | 1 | 14 |
BPSK-TJ | 0 | 0 | 35 | 5 | 960 | 0 |
QPSK-TJ | 0 | 9 | 0 | 25 | 3 | 963 |
Signal Class | BPSK | QPSK | BPSK-PJ | QPSK-PJ | BPSK-TJ | QPSK-TJ |
---|---|---|---|---|---|---|
BPSK | 999 | 1 | 0 | 0 | 0 | 0 |
QPSK | 2 | 998 | 0 | 0 | 0 | 0 |
BPSK-PJ | 0 | 0 | 1000 | 0 | 0 | 0 |
QPSK-PJ | 0 | 0 | 0 | 1000 | 0 | 0 |
BPSK-TJ | 0 | 0 | 0 | 3 | 997 | 0 |
QPSK-TJ | 0 | 0 | 0 | 5 | 0 | 995 |
Signal Class | BPSK | QPSK | BPSK-PJ | QPSK-PJ | BPSK-TJ | QPSK-TJ |
---|---|---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 | 0 | 0 |
BPSK-PJ | 0 | 0 | 1000 | 0 | 0 | 0 |
QPSK-PJ | 0 | 0 | 0 | 1000 | 0 | 0 |
BPSK-TJ | 0 | 0 | 0 | 0 | 1000 | 0 |
QPSK-TJ | 0 | 0 | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-PJ | QPSK-PJ | BPSK-TJ | QPSK-TJ |
---|---|---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 | 0 | 0 |
BPSK-PJ | 0 | 0 | 1000 | 0 | 0 | 0 |
QPSK-PJ | 0 | 0 | 0 | 1000 | 0 | 0 |
BPSK-TJ | 0 | 0 | 0 | 0 | 1000 | 0 |
QPSK-TJ | 0 | 0 | 0 | 0 | 0 | 1000 |
Signal Class | BPSK | QPSK | BPSK-PJ | QPSK-PJ | BPSK-TJ | QPSK-TJ |
---|---|---|---|---|---|---|
BPSK | 1000 | 0 | 0 | 0 | 0 | 0 |
QPSK | 0 | 1000 | 0 | 0 | 0 | 0 |
BPSK-PJ | 0 | 0 | 1000 | 0 | 0 | 0 |
QPSK-PJ | 0 | 0 | 0 | 1000 | 0 | 0 |
BPSK-TJ | 0 | 0 | 0 | 0 | 1000 | 0 |
QPSK-TJ | 0 | 0 | 0 | 0 | 0 | 1000 |
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Nawaz, T.; Alzahrani, A. Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio. Sensors 2023, 23, 7144. https://doi.org/10.3390/s23167144
Nawaz T, Alzahrani A. Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio. Sensors. 2023; 23(16):7144. https://doi.org/10.3390/s23167144
Chicago/Turabian StyleNawaz, Tassadaq, and Ali Alzahrani. 2023. "Machine-Learning-Assisted Cyclostationary Spectral Analysis for Joint Signal Classification and Jammer Detection at the Physical Layer of Cognitive Radio" Sensors 23, no. 16: 7144. https://doi.org/10.3390/s23167144