# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 2 ISAC Physical Layer Abstraction  
Wireless network simulations have been divided into link-level and system-level simulations. On the one hand, link-level simulation refers to obtaining the performance of individual links. However, since the network can grow with many devices, it can increase the complexity of emulating other interferences or environmental features. Here, system-level simulations are dedicated to evaluating the entire system's efficiency. Due to these characteristics, simulation costs can be very high in terms of time and computational costs.  
Instead of modeling all of the complex and detailed physical layer operations, its abstraction can simplify the process during system-level simulations. The PLA method replaces simulations at the link level and provides a computationally efficient way to test the desired network. Typically, the information from PLA is delivered in lookup tables containing the pre-computed process and indexing operations stored inside an array.  
Lookup tables have been widely used for PLA methods like Exponential Effective SNR Mapping (ESM), Effective SNR Mapping (ESM) [14; 17; 18; 19], or Mutual Information (MI)-ESM [20]. These methods have appeared in the predecessor technologies of 4G and 5G. We can rescue from these methods the essential elements considered to perform the link abstraction process: a channel model and an established SNR. These elements form the foundation of our abstraction method. Figure 1 illustrates a flowchart of the proposed approach. In the channel generation component, we have incorporated the creation of communication and sensing channels for the ISAC system. In addition, we have developed a prior knowledge channel, a sensing channel without the presence of the target. Details of this channel generation process will be elaborated on in the following sections.  
Following the flowchart, we determine the communication SNR, the primary input for generating the sensing algorithms. Distance, positioning, and detection algorithms show the abstraction's applicability, although other techniques or sensing services can be used. Then, this process generates lookup tables of the following Key Performance Indicators (KPIs) [15] to obtain the estimated distance, position, and detection information:  
* Position accuracy or error: This refers to the closeness of the measured position of the target or user to its true position value. In other words, the mean positioning  
Figure 1: General scheme of the proposed ISAC physical layer abstraction for sensing services.  
error in the horizontal plane or 2D reference plane can also be considered the error on the vertical axis (i.e., the altitude). This KPI ensures that the data and results are trustworthy and can be used confidently.
* False alarm probability: This describes the conditional probability of falsely detecting the presence of a target object or environment when the target object or environment is not present.
* Detection probability: Conversely, the detection probability is the conditional probability of correctly detecting the presence of a target when the target is indeed present.