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.
@article{arxiv.2303.06381,
title = {Learning to Precode for Integrated Sensing and Communications Systems},
author = {R. S. Prasobh Sankar and Sidharth S. Nair and Siddhant Doshi and Sundeep Prabhakar Chepuri},
journal= {arXiv preprint arXiv:2303.06381},
year = {2023}
}