Learning to Precode for Integrated Sensing and Communications Systems

• URL: http://arxiv.org/abs/2303.06381v1
• Date: Sat, 11 Mar 2023 11:24:18 GMT
• Title: Learning to Precode for Integrated Sensing and Communications Systems
• Authors: R.S. Prasobh Sankar, Sidharth S. Nair, Siddhant Doshi, and Sundeep Prabhakar Chepuri
• Abstract summary: We present an unsupervised learning neural model to design transmit precoders for ISAC systems. We show that the proposed method outperforms traditional optimization-based methods in presence of channel estimation errors.
• Score: 11.689567114100514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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