# AI-Enabled Compressive Spectrum Classification for Wideband Radios
## 3 Results and Discussions  
A WB of 100 MHZ, divided into 10 sub-bands, is sensed by a CR node. These sub-bands are sparsely populated by NB signals, which are generated using BPSK and QPSK  
Figure 4: Functional diagram of the proposed algorithm. Figure 3: The architecture of artificial neural network used to classify various types of signals  
modulations, and a stealthy jammer with modulated pulsed jamming capabilities is considered. The monitored WB signal \(z(t)\) is affected by AWGN and it is reconstructed through CS by using the BP algorithm. The spectrum analysis is performed at \(\alpha=2f_{c}\) to extract the expert features as AWGN has no spectral correlation due to its random nature. The classification results are shown with the help Test confusion matrix. Further, the algorithm performances are evaluated in both AWGN and multipath Rayleigh fading channels with the independent signal set from the training phase that was created using various SNRs and compression rates. An ANN architecture is selected by taking in account the procedure outlined in Section 2.3. The ANN is trained, tested, and validated with a dataset of 48,000 signals, that was created by acquiring WB signals at different compression ratios, and SNRs. Therefore, the performance of the algorithm is independent of these parameters. In Figure 5, the total accuracy of the ANN-based classifier in the existence of a pulsed modulated stealthy jammer is depicted as a test confusion matrix, demonstrating a classification accuracy of about 98.5 %. The test signals account for 15% (7200) of the entire recorded signals in the dataset. Further, it can be seen from the figure that the licit signals, BPSK and QPSK, are identified with approximately 100% accuracy, whereas for jammed bands, BPSK-Jammed and QPSK-Jammed, the accuracy is 98.3% and 96.3%, respectively. In the second phase, ANN is further tested online for the 1000 independent samples at multiple SNRs and compression ratios in the AWGN and Rayleigh fading channel. Figure 6 shows the classification rate at \(-5\) dB for different compression ratios, where, for \(K/N=0.15\) ANN classified the BPSK and QPSK with a rate of 0.63 and 0.64 respectively. On the other hand, the jammed sub-bands, BPSK-jammed and QPSK-jammed are classified with an accuracy of approximately 0.60. Then, by increasing the \(K/N\) ratio, more samples are available to reconstruct the WB signal, therefore, SCF has a better estimation compared to the previous case. Hence, accuracy is improved by increasing \(K/N\) to 0.25, it is approximately 0.86 for licit signals (BPSK to BPSK and QPSK to QPSK) and 0.88 for jammed signals (BPSK-jammed and QPSK-jammed). It is possible to infer from the figure that a classification accuracy of about 1 is obtained for \(K/N>=0.5\). On the other hand, for the Rayleigh fading channel, the classification rate is significantly low at low compression ratios (0.15-0.4) as compared to AWGN, whereas it is comparable to accuracy achieved at high compression ratios (0.5-1). Figure 5: Test confusion matrix with four target classes: BPSK, QPSK, BPSK-Jammed, and QPSK-Jammed. The algorithm's performance is also evaluated at 0 dB and 5 dB, and results are reported in Figure 7 and Figure 8, respectively. A similar trend is observed when \(K/N\) is moved from 0.15 to 1. Classification performance is increased, for instance, the classification rate at 0 dB for \(K/N=0.15\) is approximately 0.72 as compared to 0.63 at \(-5\) dB for unjammed BPSK and QPSK signals, and similarly, the trend is noticed for the signals jammed by stealthy jammer (BPSK-jammed and QPSK-jammed) and it is 0.71, compared to 0.60 at \(-5\) dB. An accuracy of approximately 100% is attained at and above \(K/N=0.25\), which shows that the ANN correctly identifies all the signals. Similarly, it can also be inferred from Figure 8 that an accuracy of 100% is also achieved for \(K/N=0.25\) at 5 dB.