标题： 图像语义通信网络的训练资源分配 显示英文标题

标题： Resource Allocation for the Training of Image Semantic Communication Networks

摘要： 语义通信是一种新范式，旨在为下一代无线网络提供更高效的通信。 它专注于传输提取出的有意义信息，而不是原始数据。 然而，基于深度学习的图像语义通信模型通常需要大量的训练时间和能量，这是不可接受的，尤其是在移动设备上。 为了解决这个挑战，我们的论文首先引入了一个分布式图像语义通信系统，其中基站和本地设备将协作训练用于上行通信的模型。 此外，我们制定了一个联合优化问题，在训练期间平衡本地设备的时间和能量消耗，同时确保有效的模型性能。 提出了一种自适应资源分配算法，以满足不同场景下的需求，并对其时间复杂度、解决方案质量和收敛性进行了深入分析。 实验结果表明，我们的算法在资源分配优化方面优于现有基准，并讨论了其对图像语义通信系统性能的影响。 显示英文摘要

摘要： Semantic communication is a new paradigm that aims at providing more efficient communication for the next-generation wireless network. It focuses on transmitting extracted, meaningful information instead of the raw data. However, deep learning-enabled image semantic communication models often require a significant amount of time and energy for training, which is unacceptable, especially for mobile devices. To solve this challenge, our paper first introduces a distributed image semantic communication system where the base station and local devices will collaboratively train the models for uplink communication. Furthermore, we formulate a joint optimization problem to balance time and energy consumption on the local devices during training while ensuring effective model performance. An adaptable resource allocation algorithm is proposed to meet requirements under different scenarios, and its time complexity, solution quality, and convergence are thoroughly analyzed. Experimental results demonstrate the superiority of our algorithm in resource allocation optimization against existing benchmarks and discuss its impact on the performance of image semantic communication systems.