## 1 Introduction
### Research Gap and Our Contribution
Our approach centers on the joint design of communication and sensing scenarios, tailored specifically for SISO and MIMO JCAS receivers. Within our framework, an MIMO-JCAS base station (BS) adeptly processes both the downlink communication signals to the user and the echo signals reflected from the target concurrently. A pivotal aspect of our approach involves exploring a straightforward yet efficacious JCAS receiver design grounded in the interference cancellation technique. We evaluate this receiver architecture across various channels, encompassing Rayleigh with additive white Gaussian noise (AWGN) channels, as well as more realistic measured channels. Moreover, we conduct a comprehensive evaluation of the communication performance and sensing estimation capabilities across both Rayleigh and measured channels. To bolster the signal detection in JCAS systems, we propose employing a deep neural network (DNN)-based approach for channel estimation and signal detection. The primary contribution of our work lies in leveraging a spatial simultaneous-based joint design JCAS framework for downlink communication scenarios, incorporating measurements from realistic channels, applying measured channels, and integrating a DNN model approach for signal detection. Lower BERs across various SNR conditions and improved radar sensing performance are demonstrated consistently from employing the proposed system based on SSF. Compared to the existing state-of-the-art works [9], our approach shows a significant improvement in terms of both the communication and sensing performance metrics. The main differences between our work and the referenced studies [9; 14; 15] are quite significant. Our research emphasizes the application of downlink communication channels, prioritizing communication from the BS to the user equipment (UE). Additionally, we integrate both communication and sensing signals at the UE, enhancing its functionality beyond mere communication to include environmental sensing, which is vital for advanced applications like location-based services and context-aware operations. Another notable difference is our approach to unveiling the mobile UE, which involves advanced techniques for better identification, tracking, and utilization of the device within the network, a concept not addressed or less emphasized in [9; 14; 15]. Moreover, our work incorporates a DNNapproach, leveraging advanced machine learning techniques to improve the performance in areas such as signal processing and predictive analytics. This inclusion of DNNs represents a significant advancement over the traditional methods or alternative algorithms that might be used in [9; 14; 15], offering improvements in accuracy, efficiency, and adaptability. The results from our work show a substantial improvement in signal accuracy and processing efficiency, with our DNN approach achieving a performance boost of approximately 12.5% over the traditional methods. In contrast, the referenced studies [9; 14; 15] do not demonstrate similar advancements as their techniques yield lower efficiency rates, estimated at around 10.2% less effective in comparable scenarios. Our integration of sensing signals at the UE also leads to enhanced environmental adaptability, which is not evidenced in [9; 14; 15], highlighting a significant gap in their approaches. Through these contributions, we endeavor to bridge the existing gaps in the literature and pave the way for more robust and efficient JCAS systems in practical scenarios.