Recently, deep autoencoders have gained traction as a powerful method for implementing goal-oriented semantic communications systems. The idea is to train a mapping from the source domain directly to channel symbols, and vice versa. However, prior studies often focused on rate-distortion tradeoff and transmission delay, at the cost of increasing end-to-end complexity and thus latency. Moreover, the datasets used are often not reflective of real-world environments, and the results were not validated against real-world baseline systems, leading to an unfair comparison. In this paper, we study the problem of remote camera pose estimation and propose AdaSem, an adaptive semantic communications approach that optimizes the tradeoff between inference accuracy and end-to-end latency. We develop an adaptive semantic codec model, which encodes the source data into a dynamic number of symbols, based on the latent space distribution and the channel state feedback. We utilize a lightweight model for both transmitter and receiver to ensure comparable complexity to the baseline implemented in a real-world system. Extensive experiments on real-environment data show the effectiveness of our approach. When compared to a real implementation of a client-server camera relocalization service, AdaSem outperforms the baseline by reducing the end-to-end delay and estimation error by over 75% and 63%, respectively.

翻译：近期，深度自编码器作为实现目标导向语义通信系统的有效方法备受关注，其核心思想是将源域数据直接映射至信道符号，并实现逆向映射。然而，现有研究多聚焦于率失真权衡与传输延迟，却导致端到端复杂度及延迟增加。此外，所采用的基准数据通常难以反映真实环境，且未与真实系统基准进行验证，导致对比缺乏公平性。本文针对远程相机姿态估计问题展开研究，提出自适应语义通信方法AdaSem，在推理精度与端到端延迟之间实现优化权衡。我们开发了自适应语义编解码模型，该模型基于隐空间分布与信道状态反馈，将源数据编码为动态数量的符号。为保持与真实系统基准相当的复杂度，我们在发射端与接收端均采用轻量级模型。基于真实环境数据的充分实验验证了方法的有效性：与客户端-服务器相机重定位服务真实实现相比，AdaSem的端到端延迟与估计误差分别降低超75%与63%。