Semantic CSI Feedback for Beam Selection: When Task-Aware Embeddings from Sparse Pilots Outperform Full-Bandwidth Reconstruction

2026-09-16

AI Summary

该研究提出了一种基于任务感知嵌入的语义CSI反馈方法，通过从稀疏导频中学习紧凑表示来优化波束选择，优于全带宽重建。

Abstract

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.

Problem

• Semantic CSI Feedback
• Beam Selection
• Task-Aware Embeddings
• Sparse Pilots
• FDD massive MIMO

Innovation

• Semantic Embedding
• Beam Selection
• Task-Aware Feedback
• Low-Dimensional Information