面向前传受限网络化ISAC的以通信为中心的符号级预编码
Communication-Centric Symbol-Level Precoding for Fronthaul-Limited Networked ISAC
- Nanjing University of Posts and Telecommunications（南京邮电大学）
- Beijing University of Posts and Telecommunications（北京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对前传受限网络化ISAC，提出压缩感知的符号级预编码框架，将前传需求与块能量关联，并采用可处理下界方法求解，仿真验证其感知性能优于线性波束成形基线。
AI中文摘要：
本文研究了面向前传受限网络化集成感知与通信（ISAC）的以通信为中心的符号级预编码（SLP）。SLP在利用建设性干扰方面具有吸引力，但其数据相关的特性使得前传感知波形设计变得困难，因为在符号块形成之前，传统线性预编码压缩模型中使用的发射协方差不可用。为解决此问题，我们开发了一个压缩感知SLP框架，将前传需求与实际块能量相关联。我们进一步制定了一个面向感知的联合设计。为求解由此产生的高维非凸问题，我们提出了一种具有性能界的可处理下界方法。仿真表明，在前传约束下，所提出的SLP方案相比线性波束成形基线提高了感知性能。
英文摘要：
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.
