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

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 the faster-than-Nyquist (FTN) signaling, where an ISAC signal is designed to simultaneously accomplish target sensing and wireless communication tasks. In particular, we minimize the minimum mean squared error (MMSE) for target parameter estimation, while guaranteeing the per-user quality-of-service by exploiting both multi-user and inter-symbol interference with constructive interference (CI) techniques. While the formulated problem is non-convex in general, we propose an efficient successive convex approximation (SCA) method, which solves a convex second-order cone program (SOCP) subproblem at each iteration. Numerical results demonstrate the effectiveness of the proposed FTN-ISAC-SLP design, showing that out method significantly outperforms conventional benchmark approaches in terms of both communication and sensing performance.