Faster-Than-Nyquist Symbol-Level Precoding for Wideband Integrated Sensing and Communications

In this paper, we present an innovative symbol-level precoding (SLP) approach for a wideband multi-user multi-input multi-output (MU-MIMO) downlink integrated sensing and communications (ISAC) system employing faster-than-Nyquist (FTN) signaling. Our proposed technique minimizes the minimum mean squared error (MMSE) for the sensed parameter estimation while ensuring the communication per-user quality-of-service through the utilization of constructive interference (CI) methodologies. While the formulated problem is non-convex in general, we tackle this issue using proficient minorization and successive convex approximation (SCA) strategies. Numerical results substantiate that our FTN-ISAC-SLP framework can increase communication throughput by up to 20% while reducing sensing MMSE by about 1 dB.