## 3 Simulation Results
### Joint Design System Based on Deep Learning
#### 3.8.2 DNN Trade-Off Analysis  
In systems that combine communication and sensing functionalities, optimizing both aspects involves a delicate trade-off. Communication systems typically aim to maximize data throughput, minimize latency, and ensure reliable transmission, often at the expense of energy consumption. On the other hand, sensing systems focus on accurately detecting and interpreting environmental signals, requiring precise measurements and minimal interference. Balancing these conflicting objectives is crucial in designing efficient joint communication and sensing systems. Deep learning methodologies offer promising solutions to navigate this intricate balance. By leveraging DNNs, these systems can adaptively adjust their behavior based on dynamic environmental conditions and performance requirements. One key advantage of deep learning is its ability to jointly optimize multiple objectives, allowing the system to find a balance between the communication and sensing performance. This is particularly valuable in scenarios where traditional optimization techniques may struggle to find satisfactory solutions due to the complexity of the problem space. Formulating objective functions is a fundamental aspect of optimizing joint communication and sensing systems. These objective functions capture the trade-off between communication and sensing performance by considering relevant metrics such as BER for communication and MSE for sensing. By appropriately weighting these metrics based on the system requirements and priorities, objective functions guide the optimization process towards achieving a desirable balance between the two objectives. Multi-objective optimization techniques further enhance the optimization process by simultaneously considering multiple conflicting objectives. These techniques explore the trade-off space to identify Pareto-optimal solutions, which represent the best compromise between communication and sensing performance. By providing a comprehensive view of the trade-offs involved, multi-objective optimization helps designers to make informed decisions about the system design and parameter tuning. To optimize the overall performance of the joint design system, it is necessary to analyze the trade-offs between different operational modes. Here, we conduct a comprehensive analysis, including a key system metrics comparison between the traditional DCAS and SSF D-JCAS under DL application. This comparison helps us to realize how changes in the communication SNR when applying the DL approach affect the BER of DCAS compared to SSF D-JCAS. Consistently, the channel is Rayleigh with AWGN, and the modulation scheme is QPSK. The simulation parameters are kept consistent, featuring four transmit antennas (\(N_{t}=4\)) and either one or two receive antennas (\(N_{r}=1,2\)). Figure 19 illustrates the BER for communication performance and MSE for sensing performance across DL approaches for both the traditional DCAS and SSF D-JCAS, with fixed sensing power and varying communication power. Here, the scenario involves four transmit antennas (\(N_{t}=4\)) and one receive antenna (\(N_{r}=1\)). In the graph, it is evident that SSF D-JCAS exhibits a lower BER compared to the traditional DCAS for downlink transmissions when the DNN is applied to both systems. Furthermore, when the SNR is high, the BER curves for SSF D-JCAS and DCAS closely align and coincide properly. The concurrent transmission of the communication and radar signals in SSF D-JCAS explains this phenomenon. In the SSF D-JCAS scheme, the received signal at the UE consists of both communication and radar signals transmitted simultaneously over the same channel. Conversely, the traditional DCAS only covers the communication signal, along with AWGN. Thus, the BER performance of SSF D-JCAS outperforms that of the traditional DCAS. Conversely, when applying DNN, the traditional DCAS reveals a higher MSE of the reflection coefficient compared to SSF D-JCAS.