# Symbol-Level Precoding for Integrated Sensing and Communications: A Faster-Than-Nyquist Approach

## TL;DR

This paper introduces a symbol-level precoding method for multi-user MIMO ISAC systems using faster-than-Nyquist signaling, enhancing both sensing accuracy and communication quality.

## Contribution

It presents a novel FTN-based SLP technique that optimizes target estimation and user QoS simultaneously, employing an efficient SCA approach for non-convex problems.

## Key findings

- Significant performance improvements over benchmarks in sensing accuracy.
- Enhanced communication quality with guaranteed QoS.
- Effective handling of non-convex optimization in FTN-ISAC systems.

## Abstract

In this paper, we propose a novel symbol-level precoding (SLP) method for a multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system based on faster-than-Nyquist (FTN) signaling. Our method minimizes the minimum mean squared error (MMSE) for target parameter estimation while guaranteeing per-user quality-of-service by exploiting constructive interference (CI) techniques. We tackle the non-convex problem using an efficient successive convex approximation (SCA) method. Numerical results demonstrate that our FTN-ISAC-SLP design significantly outperforms conventional benchmarks in both communication and sensing performance.