## 3 Simulation Results
### Radar-Based Deep Learning  
After performing the DNN application for signal detection, we obtain the predicted received signal, which is then used for demodulation to derive predicted received symbols. These symbols are compared with the transmitted symbols to determine the symbol error rate (SER) or bit error rate (BER) later in order to evaluate the communication performance. At this stage, before being demodulated to obtain the predicted symbols, the received signal generated by the DNN is utilized to calculate the estimation range and velocity of a target in the radar signal processing stage instead of comparing it with the transmitted symbols. The root mean square error (RMSE) is used to evaluate the radar sensing performance for both the traditional and DL approaches. The channel is assumed to be Rayleigh with additive white Gaussian noise (AWGN), and the modulation scheme is QPSK. Figures 16 and 17 illustrate the range RMSE and velocity RMSE over SNR for both the traditional approach and the DNN case considering different ranges and velocities, as referenced in the 5G literature [18]. When the actual target range is 30 m, the estimated target range for the traditional approach is 31.02 m, while, for the DNN approach, it is 30.517 m. This difference can be attributed to the traditional approach possibly suffering from inaccuracies or noise in the received signal, leading to a slightly higher estimated range. Conversely, the DNN approach, leveraging its learning capabilities, tends to provide a more accurate estimation by effectively capturing and processing the signal characteristics. Similarly, when the actual target velocity is 30 m/s, the estimated target velocity for the traditional approach is 31.746 m/s, compared to 31.250 m/s for the DNN approach. This discrepancy could arise from the traditional approach's reliance on conventional algorithms, which may struggle to handle complex signal variations, resulting in slightly inflated velocity estimates. On the other hand, the DNN approach, being trained on diverse data, demonstrates better adaptability to varying signal conditions, leading to more reliable velocity estimates. Referring to the 5G beyond specification [18], when the range of a target is 305 m away from the BS and the speed of a target increases to 140 m/s, the range and velocity RMSE over SNR for the two cases are depicted in Figure 17. When the actual target range is 305 m, the estimated target range for the traditional approach is 306.317 m, while, for the DNN approach, it is 306.297 m. This slight variation could be due to the inherent noise and uncertainties present in the signal processing of both approaches, with the traditional approach possibly being more susceptible to these factors. Furthermore, when the actual target velocity is 140 m/s, the estimated target velocity for the traditional approach is 134.421 m/s, compared to 134.442 m/s for the DNN approach. Again, this discrepancy underscores the DNN's ability to better handle signal variations and noise, resulting in more accurate velocity estimates compared to the traditional approach. Figure 16: Range and velocity RMSEs over SNR for the traditional and DNN approaches with range = 30 m and velocity = 30 m/s. Figure 17: Range and velocity RMSEs over SNR for the traditional and DNN approaches with range = 305 m and velocity = 140 m/s. Thus, the performance of the DNN approach versus the traditional approach in estimating target range and velocity is significant. Below are the detailed comparisons between the two methods. * **Range Estimation:** The DNN approach outperforms the traditional approach in estimating target range due to its ability to capture complex patterns and relationships in the data.