Wideband ISAC Optimization with Faster-Than-Nyquist Symbol-Level Precoding

In this paper, we introduce a novel symbol-level precoding (SLP) methodology for a wideband multi-user multi-input multi-output (MU-MIMO) downlink Integrated Sensing and Communications (ISAC) system leveraging faster-than-Nyquist (FTN) signaling. The proposed approach optimizes the minimum mean squared error (MMSE) for sensed parameter estimation while maintaining per-user communication quality-of-service, capitalizing on constructive interference (CI) techniques. While the ensuing problem manifests as non-convex in nature, we adeptly manage this challenge through judicious employment of minorization strategies. Numerical outcomes corroborate that our method substantially ameliorates communication throughput whilst preserving adequate sensing performance.