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:在这项工作中,我们研究了面向异构无线网络的按需语义通信的具有挑战性的问题。我们提出了一种保真度可调的语义传输框架(FAST),使无线设备能够在不同的应用场景和资源条件下高效地发送数据。为此,我们首先设计了一个动态子模型训练方案,以学习灵活的语义模型,使边缘设备能够使用不同宽度的语义模型自定义传输保真度。在此之后,我们关注FAST优化问题,以最小化系统的能量消耗,并考虑延迟和保真度约束。其次,我们为设备推导了最佳的传输策略,包括语义模型的缩放因子、计算频率和传输功率。实验结果表明,与基线传输方案相比,所提出的框架可以在保持合理的数据保真度的前提下,将系统的能耗和数据大小降低了一个量级。