基于信道和内容偏好反馈的协作语义通信网络性能优化
Optimization of Collaborative Semantic Communication Network Performance with Channel and Content Preference Feedback
AI总结：
针对现有语义通信框架问题，提出含基站与用户协作及反馈机制的语义通信框架，通过优化子信道等减少原始与再生图像语义加权均方误差，提出VDAC-DNC方案提升性能，相比其他方法有显著提高。
AI中文摘要：
现有语义通信框架对所有图像区域同等对待，这在实际应用中不实用。本文提出一种新颖的语义通信框架，基站将图像划分为子图像，提取语义信息并按用户偏好传输。用户根据接收的子图像重建图像，并在动态信道和有限资源下协作决定何时发送信道状态信息或内容偏好反馈。通过优化子信道分配、用户功率分配和反馈选择，最小化原始图像与再生图像之间的语义加权均方误差。提出了一种具有动态邻域构建的价值分解演员-评论家（VDAC-DNC）方案，该方案结合AC与价值分解网络，允许基站通过连续动作分布近似离散动作，减少输出维度并提高训练效率。引入的DNC方法通过构建小的离散邻域动作空间来搜索具有最大Q值的动作，避免遍历大的离散动作空间。仿真结果表明，与标准多智能体QAC方法和无反馈传输的方法相比，所提出的VDAC-DNC方案性能分别提高了5.04%和18.55%。
英文摘要：
Existing semantic communication frameworks treat and transmit all image regions with equal importance, which is not practical for real-world applications which may prioritize different content in an image. To address this issue, we propose a novel semantic communication framework that enables a transmitter to use limited channel and content feedback to prioritize the transmission of important image regions. In particular, in the proposed framework, a base station (BS) divides each image into sub-images, extracts their semantic information, and transmits them to users according to their preferences. The users will reconstruct the image based on the received sub-images and cooperatively decide when to send channel state information (CSI) or content-preference feedback under dynamic channels and limited resources. We formulate an optimization problem to minimize the semantic-weighted mean square error between the original image and the regenerated image by optimizing sub-channel allocation, users' power allocation, and feedback selection. To address this problem, a value decomposition actor- critic (AC) with dynamic neighborhood construction (VDAC-DNC) scheme is proposed. The proposed method combines AC with value decomposition networks to allow the BS to approximate discrete actions by a continuous action distribution, thus reducing the output dimension and improving training efficiency. The introduced DNC method further improves training efficiency by constructing a small discrete neighboring action space to search for an action with the maximum Q value, thus avoiding traversing the large discrete action space. Simulation results show that the proposed VDAC-DNC scheme can improve the performance by up to 5.04% and 18.55% compared to the standard multi-agent QAC method and the proposed method without feedback transmission.
