面向资源高效的语义编码方案：流形约束的超连接
Resource-efficient Semantic Coding Schemes with Manifold-constrained Hyper-connections
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中文总结 AI 辅助
针对语义通信和面向任务通信的资源高效需求，提出带熵瓶颈的流形约束超连接编码方案，可提升性能与鲁棒性且不增加额外信道资源。
中文摘要 AI 辅助
语义通信（SemCom）和面向任务的通信（TOC）通过聚焦传输语义或与任务相关的信息而非原始消息，可降低无线资源消耗。实际应用中，核心挑战是在保持信息紧凑性的同时，确保传输信息对信道噪声和衰落具有鲁棒性。现有基于学习的收发器常通过使用更大的编码器或更高维的信道特征来提升可靠性，这会增加计算复杂度和信道 uses。因此，优化的系统设计需要显式速率控制以平衡性能与传输资源（如带宽和功率）。为此，我们提出一种带熵瓶颈（EB）的流形约束超连接（mHC）编码方案，用于无线信道上资源高效的SemCom和TOC。与现有编码器的单个残差路径不同，所提出的基于mHC的语义编码器应用多个残差流，并通过双随机（DS）混合矩阵约束它们的交互。这种新结构以可忽略的参数和浮点开销提升了表示多样性和训练稳定性。EB对信道特征进行量化并估计熵编码速率，实现了带宽和发射功率约束下的端到端速率-失真/任务优化。我们进一步证明，DS约束的流混合不会增加传输特征的微分熵，这意味着理想的EB编码长度不会增加。在加性高斯白噪声（AWGN）、瑞利衰落、莱斯衰落和不完善信道状态信息（CSI）下的SemCom和TOC实验表明，与残差和无约束HC基线相比，所提方案在不增加额外信道 uses 的情况下，提升了语义/任务性能、通信鲁棒性和收敛稳定性。
英文摘要
Semantic communication (SemCom) and task-oriented communication (TOC) can reduce wireless resource consumption by focusing on transmitting semantic or task-relevant information instead of raw messages. In practice, a main challenge is to make transmitting information robust to channel noise and fading while keeping it compact. Existing learning-based transceivers often improve reliability by using larger encoders or higher-dimensional channel features, which increase computation complexity and channel uses. Therefore, optimized system design needs explicit rate control to balance performance and transmitting resources e.g., bandwidth and power. For this purpose, we propose a manifold-constrained hyper-connection (mHC) coding scheme with an entropy bottleneck (EB) for resource-efficient SemCom and TOC over wireless channels. Instead of using a single residual path of existing encoders, the proposed mHC-based semantic encoder applies multiple residual streams and constrains their interaction by doubly stochastic (DS) mixing matrices. The new structure improves representation diversity and training stability with negligible parameter and floating-point overhead. The EB quantizes the channel features and estimates the entropy-coded rate, enabling end-to-end rate--distortion/task optimization under bandwidth and transmit-power constraints. We further show that DS-constrained stream mixing does not increase the differential entropy of the transmitted features. This implies no increase in the ideal EB coding length. Experiments on SemCom and TOC under additive white Gaussian noise (AWGN), Rayleigh fading, Rician fading, and imperfect channel state information (CSI) show that the proposed scheme improves semantic/task performance, communication robustness, and convergence stability over residual and unconstrained HC baselines, while requiring no additional channel uses.
