LISAC: Learned Coded Waveform Design for ISAC with OFDM

TL;DR

LISAC introduces a deep learning-based waveform design for ISAC with OFDM, jointly optimizing communication and sensing performance through end-to-end training of neural networks, achieving improved trade-offs in diverse scenarios.

Contribution

The paper presents a novel learned coded waveform for ISAC that integrates RNN-based encoding, neural channel calibration, and dual sensing loss functions, advancing joint waveform design.

Findings

Achieves better trade-offs between sensing and communication performance.

Demonstrates effectiveness in AWGN and multi-path fading scenarios.

Shows gains from each design component through ablation studies.

Abstract

We propose deep learning based coded waveform design for integrated sensing and communication (ISAC) with orthogonal frequency-division multiplexing (OFDM). Our goal is to design a coded waveform capable of delivering accurate target parameter estimation while maintaining high communication quality measured in terms of bit error rate (BER). In the proposed learned coded waveform for ISAC (LISAC), the pilot and data encoding functions at the encoder are parameterized by recurrent neural networks (RNNs) and are trained jointly in an end-to-end fashion. The communication receiver estimates the channel and performs residual-assisted minimum mean square error (MMSE) channel equalization, where a neural network is introduced to calibrate the coarse estimate produced by the standard MMSE channel equalizer. Then, an RNN-based channel decoder is employed to decode the information bits using the equalized signal. Two different sensing loss functions are considered, one calculates the mean square error (MSE) between the original and the estimated sensing parameters, while the other calculates the Cramer-Rao lower bound (CRLB). The LISAC modules are optimized using a weighted combination of communication and sensing losses and different trade-off points between the sensing and communication performances are achieved by adjusting the weights. Simulation results show that the proposed LISAC waveform achieves a better trade-off curve compared to existing alternatives for both AWGN and multi-path fading scenarios. Ablation studies are carried out to demonstrate the gain brought by each design component for a better understanding of the proposed scheme.