  Integrated sensing and communication (ISAC) is an encouraging wireless
technology which can simultaneously perform both radar and communication
functionalities by sharing the same transmit waveform, spectral resource, and
hardware platform. Recently emerged symbol-level precoding (SLP) technique
exhibits advancement in ISAC systems by leveraging the waveform design degrees
of freedom (DoFs) in both temporal and spatial domains. However, traditional
SLP-based ISAC systems are designed in a modular paradigm, which potentially
limits the overall performance of communication and radar sensing. The high
complexity of existing SLP design algorithms is another issue that hurdles the
practical deployment. To break through the bottleneck of these approaches, in
this paper we propose an end-to-end approach to jointly design the SLP-based
dual-functional transmitter and receivers of communication and radar sensing.
In particular, we aim to utilize deep learning-based methods to minimize the
symbol error rate (SER) of communication users, maximize the detection
probability, and minimize the root mean square error (RMSE) of the target angle
estimation. Multi-layer perceptron (MLP) networks and a long short term memory
(LSTM) network are respectively applied to the transmitter, communication users
and radar receiver. Simulation results verify the feasibility of the proposed
deep-learning-based end-to-end optimization for ISAC systems and reveal the
effectiveness of the proposed neural networks for the end-to-end design.
