面向太赫兹通信的语义感知子带分配
Semantic-Aware Sub-Band Allocation for Terahertz Communications
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中文总结 AI 辅助
针对太赫兹通信中子带质量不均的问题，本文提出基于SBERT和模仿学习的语义感知子带分配方法，性能优于基准方案且接近神谕水平。
中文摘要 AI 辅助
本文研究太赫兹（THz）通信系统中的语义感知子带分配问题，其中频率选择性分子吸收会造成高度不均匀的子带质量。与传统方案不同，语义保真度非线性依赖于信噪比（SNR），且具有句子特异性，形成了不可分离的分配问题，无法通过简单的基于排序的策略求解。为解决该问题，我们采用基于句子-BERT（SBERT）的代理模型，从句子嵌入和子带SNR预测语义保真度；提出一种重要性感知调度器，利用捕获句子-子带对重要性加权语义相似度的神谕效用函数，将句子分配到对应子带。随后通过模仿学习训练神经调度器，其运行时比全DeepSC基神谕评估低200倍以上。与深度学习语义通信（DeepSC）系统集成后，所提方法在实际THz信道条件下，始终优于所有基准方案，且接近神谕级性能。
英文摘要
This paper studies semantic-aware sub-band al- location for terahertz (THz) communication systems, where frequency-selective molecular absorption creates highly non- uniform sub-band qualities. Unlike conventional formulations, semantic fidelity depends nonlinearly on the signal-to-noise ratio (SNR) and is also sentence-specific, leading to a non-separable assignment problem that is generally not solvable using simple ordering-based policies. To address this, we use a sentence-BERT (SBERT)-based surrogate model that predicts semantic fidelity from the sentence embedding and sub-band SNR. We propose an importance-aware scheduler that assigns sentences to sub- bands based on their semantic contribution using an oracle utility function that captures importance-weighted semantic similarity across sentence-sub-band pairs. A neural scheduler is then trained through imitation learning to approximate the oracle policy at more than 200x lower runtime than full DeepSC- based oracle evaluation. Integrated with a deep-learning-enabled semantic communication (DeepSC) system, the proposed method consistently outperforms all benchmark schemes and approaches oracle-level performance under realistic THz channel conditions.
发表机构
- American University of Beirut（贝鲁特美国大学）
机构由 AI 辅助整理，请以论文原文为准。
