End-to-End Learning for Integrated Sensing and Communication

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.

A Tutorial on Terahertz-Band Localization for 6G Communication Systems

“…Each subtask can be solved using learning-based methods with a much smaller training dataset. In the first stage, learning-based methods can be used to reduce the effect of the hardware impairments (HWIs) such as antenna spacing error [197], IQI [198], mutual coupling (MC) [199], and power amplifier nonlinearity (PAN) [200]. The distorted signal due to the impairments can be recovered or compensated during the data pre-processing stage.…”