In this paper, we present an unsupervised learning neural model to design transmit precoders for integrated sensing and communication (ISAC) systems to maximize the worst-case target illumination power while ensuring a minimum signal-to-interference-plus-noise ratio (SINR) for all the users. The problem of learning transmit precoders from uplink pilots and echoes can be viewed as a parameterized function estimation problem and we propose to learn this function using a neural network model. To learn the neural network parameters, we develop a novel loss function based on the first-order optimality conditions to incorporate the SINR and power constraints. Through numerical simulations, we demonstrate that the proposed method outperforms traditional optimization-based methods in presence of channel estimation errors while incurring lesser computational complexity and generalizing well across different channel conditions that were not shown during training.

翻译：在本文中,我们提出了一个未经监督的学习神经模型,用于设计综合遥感和通信系统预言器,以最大限度地实现最坏情况目标照明能力,同时确保所有用户的最低信号到干涉加噪音比率(SINR),从上链接试验和回声中传输预言器的问题可被视为一个参数化功能估计问题,我们提议使用神经网络模型来学习这一功能。为了学习神经网络参数,我们根据第一阶最佳条件开发了一个新的损失函数,以纳入SIRN和动力限制。我们通过数字模拟,我们证明拟议方法在出现频道估计错误时比传统的优化方法要好,同时造成较少的计算复杂性,并在培训期间没有显示的不同频道条件下加以全面推广。</s>