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：近年来，深度自编码器已成为实现目标导向语义通信系统的一种有效方法。其核心思想是训练从源域直接到信道符号的映射及其逆映射。然而，先前的研究往往侧重于率失真权衡与传输延迟，代价是增加了端到端复杂度进而导致时延上升。此外，所用数据集通常不能反映真实环境，且结果未在真实世界基线系统上进行验证，导致比较有失公允。本文研究远程相机姿态估计问题，提出AdaSem——一种自适应语义通信方法，旨在优化推理精度与端到端时延之间的权衡。我们开发了一种自适应语义编解码模型，该模型基于潜在空间分布和信道状态反馈，将源数据编码为动态数量的符号。我们在发射端和接收端均采用轻量级模型，以确保其复杂度与真实系统实现的基线方案相当。在真实环境数据上的大量实验证明了我们方法的有效性。与客户端-服务器相机重定位服务的实际实现相比，AdaSem将端到端时延和估计误差分别降低了75%和63%以上，性能显著优于基线。