End-to-End Learning for Integrated Sensing and Communication

2021/11/03 by José Miguel Mateos-Ramos, Mateos-Ramos, José Miguel, Jinxiang Song +11 · 4 citations
Earth and Planetary Sciences · Engineering · #Advanced SAR Imaging Techniques #FOS: Electrical engineering #Radar Systems and Signal Processing #Signal Processing (eess.SP) #Underwater Acoustics Research #eess.SP #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2111.02106

6 pages, 5 figures, submitted to ICC

arxiv created 2021/11/03 · openalex publication_date 2021/11/03 · arxiv updated 2021/11/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.

Cited by

• Neural Network-Based Single-Carrier Joint Communication and Sensing: Loss Design, Constellation Shaping and Precoding
• Deep Learning-based Techniques for Integrated Sensing and Communication Systems: State-of-the-Art, Challenges, and Opportunities
• Integrated Radio Sensing Capabilities for 6G Networks: AI/ML Perspective
• AI-Empowered Integrated Sensing and Communications

Related

• End-to-End Learning for SLP-Based ISAC Systems
• Joint Waveform and Beamforming Design in RIS-ISAC Systems: A Model-Driven Learning Approach
• MSE-Based Training and Transmission Optimization for MIMO ISAC Systems
• Model-Based End-to-End Learning for Multi-Target Integrated Sensing and Communication
• Semi-Supervised End-to-End Learning for Integrated Sensing and Communications