发表机构
Ericsson Research（爱立信研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出语义CSI反馈方法，用稀疏导频的紧凑嵌入替代全带宽重构，在波束选择任务上超越CsiNet，验证了“传输意图而非信号”的语义通信原则。
AI 中文摘要
在FDD大规模MIMO系统中，经典CSI反馈传输信道的压缩重构，无论下游任务如何，都优化对原始信号的保真度。我们提出一种语义通信视角：UE不重构信道，而是传输一个经过端到端优化、用于gNB波束选择的学得“语义嵌入”。通过对比面向重构的反馈（CsiNet）与任务感知的语义反馈，在两种输入域和三种观测场景下，我们表明：仅从角延迟域的43个NR CSI-RS导频中提取的d=8个实值语义嵌入，即可实现最高的波束预测精度，优于所有能访问完整512子载波信道的方法。关键见解在于，波束相关信息本质上是低维的：语义编码器学会丢弃与重构无关的结构，仅保留与波束选择相关的紧凑表示，实现了语义通信的核心原则：传输意图，而非信号。
英文摘要
Classical CSI feedback in FDD massive MIMO transmits a compressed reconstruction of the channel, optimizing fidelity to the original signal regardless of the downstream task. We propose a semantic communication perspective: instead of reconstructing the channel, the UE transmits a learned \emph{semantic embedding} optimized end-to-end for beam selection at the gNB. Comparing reconstruction-oriented feedback (CsiNet) against task-aware semantic feedback across two input domains and three observation scenarios, we show that a semantic embedding of just $d=8$ real values from only 43 NR CSI-RS pilots in the angular-delay domain achieves the highest beam prediction accuracy, outperforming every method with access to the full 512-subcarrier channel. The key insight is that beam-relevant information is intrinsically low-dimensional: the semantic encoder learns to discard reconstruction-irrelevant structure and retain only a compact representation that is relevant to beam selection, realizing the core principle of semantic communication: transmit the intent, not the signal.
