Learning to Precode for Integrated Sensing and Communications Systems
This work addresses a domain-specific challenge in wireless communication systems, offering an incremental improvement over existing methods.
The paper tackles the problem of designing transmit precoders for integrated sensing and communication systems to maximize target illumination power while ensuring minimum SINR for users, and demonstrates that the proposed unsupervised neural model outperforms traditional optimization-based methods in computational complexity and generalization across unseen channel conditions.
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.