This paper characterizes the fundamental limits of integrated sensing and communication (ISAC) when the transmitter is subject to an artificial intelligence (AI) representation bottleneck and the receiver employs a fluid antenna system (FAS). Specifically, the message is first encoded into an ideal Gaussian waveform and mapped by an AI encoder into a finite-capacity latent representation that constitutes the physical channel input, while the FAS receiver selects the port experiencing the most favorable channel conditions. We reveal that the AI bottleneck is equivalent to an additive representation noise, which reduces both the communication and sensing signal-to-noise ratios (SNRs) at the selected port. We then derive the resulting ISAC capacitydistortion region and establish tight converse and achievability bounds under general fading models, including Jakes-correlated channels. Leveraging the spatial degrees of freedom (DoF) characterization of the Jakes' model, we furthermore prove that the port-selection gain is fundamentally constrained by the physical length of the FAS region: the effective diversity order equals the numerical rank of the Jakes' correlation matrix and increases only with the FAS length. Consequently, enlarging the FAS length allows the selected-port SNR to approach the AI-imposed ceiling, driving the achievable communication rate and sensing mean square error (MSE) toward their AI-limited fundamental bounds. Numerical results corroborate the analysis and scaling laws.

翻译：本文刻画了当发射端受限于人工智能表示瓶颈、接收端采用流体天线系统时，集成感知与通信的基本性能极限。具体而言，消息首先被编码为理想高斯波形，再通过AI编码器映射为有限容量的潜在表示作为物理信道输入；而FAS接收端则选择信道条件最优的端口。我们揭示了AI瓶颈等效于附加表示噪声，该噪声会降低所选端口的通信与感知信噪比。随后，我们推导出由此产生的ISAC容量-失真区域，并在包括Jakes相关信道在内的一般衰落模型下建立了紧致的逆界与可达界。借助Jakes模型的空间自由度刻画，我们进一步证明端口选择增益从根本上受限于FAS区域的物理长度：有效分集阶数等于Jakes相关矩阵的数值秩，且仅随FAS长度增加而提升。因此，增大FAS长度可使所选端口信噪比逼近AI设定的上限，从而驱动可达通信速率与感知均方误差趋近其AI受限的基本界。数值结果验证了理论分析与标度律。