Communication-Centric Symbol-Level Precoding for Fronthaul-Limited Networked ISAC

Abstract

This paper studies communication-centric symbol-level precoding (SLP) for fronthaul-limited networked integrated sensing and communication (ISAC). SLP is attractive for exploiting constructive interference, but its data-dependent nature makes fronthaul-aware waveform design difficult because the transmit covariance used in conventional linear-precoding compression models is unavailable before the symbol block is formed. To address this issue, we develop a compression-aware SLP framework that relates the fronthaul requirement to the realized block energy. We further formulate a sensing-oriented joint design. To solve the resulted high-dimensional and nonconvex problem, we propose a tractable lower-bound method with a performance bound. Simulations show that the proposed SLP scheme improves sensing performance over linear-beamforming baselines under fronthaul constraints.