Integrated Sensing and Communication (ISAC) is gradually becoming a reality due to the significant increase in frequency and bandwidth of next-generation wireless communication technologies. Therefore it becomes crucial to evaluate the communication and sensing performance using appropriate channel models to address resource competition from each other. Existing work only models the sensing capability based on the mutual information between the channel response and the received signal, and its theoretical resolution is difficult to support the high-precision requirements of ISAC for sensing tasks, and may even affect its communication optimal. In this paper, we propose a sensing channel encoder model to measure the sensing capacity with higher resolution by discrete task mutual information. For the first time, derive upper and lower bounds on the sensing accuracy for a given channel. This model not only provides the possibility of optimizing the ISAC systems at a finer granularity and balancing communication and sensing resources, but also provides theoretical explanations for classical intuitive feelings (like more modalities more accuracy) in wireless sensing. Furthermore, we validate the effectiveness of the proposed channel model through real-case studies, including person identification, displacement detection, direction estimation, and device recognition. The evaluation results indicate a Pearson correlation coefficient exceeding 0.9 between our task mutual information and conventional experimental metrics (e.g., accuracy).

翻译：随着下一代无线通信技术在频率和带宽上的显著提升，集成感知与通信（ISAC）正逐步成为现实。因此，采用合适的信道模型来评估通信与感知性能，以解决二者之间的资源竞争问题变得至关重要。现有研究仅基于信道响应与接收信号之间的互信息对感知能力进行建模，其理论分辨率难以满足ISAC在感知任务中的高精度需求，甚至可能影响其通信最优性。本文提出一种感知信道编码器模型，通过离散任务互信息以更高分辨率测量感知容量。首次推导出给定信道下感知精度的上下界。该模型不仅为在更细粒度上优化ISAC系统、平衡通信与感知资源提供了可能，也为无线感知中经典的直观经验（如模态越多精度越高）提供了理论解释。此外，我们通过实际案例研究验证了所提信道模型的有效性，包括人员识别、位移检测、方向估计和设备识别。评估结果表明，我们的任务互信息与传统实验指标（如准确率）之间的皮尔逊相关系数超过0.9。