# AI-Enabled Compressive Spectrum Classification for Wideband Radios
## 3 Results and Discussions
The results show that even at low SNR \(-5\) dB and 50 % of Nyquist rate samples, the algorithm correctly classified all the signals to their targeted classes. Further, it is possible to get an error-free classification by using only 25 % of Nyquist rate samples at 0 dB to reconstruct the WB signal. The algorithm obtained such a high performance because SCF of various types of communications signals produces highly diverse patterns, and hence yields highly distinct feature vectors. Further, the algorithm is shown as a robust and energy-efficient tool to recognize signals at low SNRs and compression ratios. Our novel algorithm ensures high accuracy when compared to techniques reported in the literature [32; 39; 40; 41; 42] that required 10-20 dB SNR to achieve comparable performances. Further, the performance of our algorithm is compared to work in [43] where classification was performed using the traditional databases matching method. The authors used the same set of signals as considered in our work. They acquired signals only at 0 dB by varying compression ratio between 0.25 to 1 under the AWGN channel conditions. Figure 9 shows that embedding the ANN to the spectrum characterization algorithm not only increased the signal classification accuracy but also remarkably increased the detection rates for the jammers. The detection rate for the jammer is about 1 for \(K/N=0.25\) at 0 dB as compared to 0.45 (average) in database parameters matching methods. Moreover, ref. [43] could only able to detect the jammer with a rate of 0.9 at Nyquist rate sampling. On  
Figure 6: Classification accuracy of the algorithm at \(-5\) dB in the AWGN and Rayleigh fading channel. the other hand, in [32], the algorithm used features like bandwidths, center frequencies, and maximum amplitude of the PSDs of the signals for the naive Bayes Classifier (NBC). They acquired the signals at 5 dB and 10 dB by varying the compression ratios between 0.7 and 1 under the Rayleigh fading channel. Our algorithm performance is compared for Rayleigh fading channel with this technique in Table 2 and the result shows that our newly developed algorithm outperforms this technique both in terms of jammer detection and signal classification rates. The algorithm in [32] is able to classify the un-jammed modulated signals BPSK and QPSK at 5 dB with a rate of 0.76, 0.80, 8.80, and 0.82 for K/N of 0.70, 0.80, 0.90 and 1, respectively. Similarly, for BPSK-Jammed and QPSK-Jammed, the jammer detection rate is 0.40, 0.48, 0.59, and 0.70 for K/N of 0.70, 0.80, 0.90, and 1, respectively. Our approach, on the other hand, obtained signal classification and jammer detection rates of 1 for all K/N (0.7 to 1). Further, it can be noticed from Figures 6 and 7 that our algorithm obtained a total classification rate of approximately 0.9 and 0.95 at  
Figure 8: Classification accuracy of the algorithm at 5 dB in the AWGN and Rayleigh fading channel. Figure 9: Performance comparison with detection algorithm in [43]. The algorithm in [43] used a conventional database matching method for signal classification, whereas our proposed algorithm used an artificial neural network as a classifier.