In this work, we investigate the challenging problem of on-demand semantic communication over heterogeneous wireless networks. We propose a fidelity-adjustable semantic transmission framework (FAST) that empowers wireless devices to send data efficiently under different application scenarios and resource conditions. To this end, we first design a dynamic sub-model training scheme to learn the flexible semantic model, which enables edge devices to customize the transmission fidelity with different widths of the semantic model. After that, we focus on the FAST optimization problem to minimize the system energy consumption with latency and fidelity constraints. Following that, the optimal transmission strategies including the scaling factor of the semantic model, computing frequency, and transmitting power are derived for the devices. Experiment results indicate that, when compared to the baseline transmission schemes, the proposed framework can reduce up to one order of magnitude of the system energy consumption and data size for maintaining reasonable data fidelity.

翻译：本文研究了异构无线网络中按需语义通信这一具有挑战性的问题。我们提出了一种信保真度可调语义传输框架（FAST），使无线设备能够在不同应用场景和资源条件下高效发送数据。为此，我们首先设计了一种动态子模型训练方案来学习灵活的语义模型，使边缘设备能够通过调整语义模型的宽度来定制传输保真度。随后，我们聚焦于FAST优化问题，在满足延迟和保真度约束的前提下最小化系统能耗。进而推导出设备的最优传输策略，包括语义模型的缩放因子、计算频率和发射功率。实验结果表明，与基准传输方案相比，所提框架可在维持合理数据保真度的同时，将系统能耗和数据量降低一个数量级。