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In this paper, we present an unsupervised learning neural model to designtransmit precoders for integrated sensing and communication (ISAC) systems tomaximize the worst-case target illumination power while ensuring a minimumsignal-to-interference-plus-noise ratio (SINR) for all the users. The problemof learning transmit precoders from uplink pilots and echoes can be viewed as aparameterized function estimation problem and we propose to learn this functionusing a neural network model. To learn the neural network parameters, wedevelop a novel loss function based on the first-order optimality conditions toincorporate the SINR and power constraints. Through numerical simulations, wedemonstrate that the proposed method outperforms traditional optimization-basedmethods in presence of channel estimation errors while incurring lessercomputational complexity and generalizing well across different channelconditions that were not shown during training.

Learning to Precode for Integrated Sensing and Communications Systems