AI 中文总结
本文针对多用户语义通信的用户异质性问题，提出能量感知压缩计算协同适配（CoCo）框架，通过鲁棒编解码器与贪心子载波分配降低延迟，可满足各用户需求。
AI 中文摘要
基于深度联合信源信道编码（DeepJSCC）的语义通信（SemCom）在低信道带宽比下可提供高感知质量，是下一代无线网络的核心技术。但现有研究难以适配用户在信道质量、期望服务质量（QoS）目标及本地可用能量方面的异质性。为此，本文明确反映用户设备在期望QoS、信道状况和本地能量上的差异，并对该问题进行数学建模。接着，提出能量感知压缩计算协同适配（CoCo）框架，基站可通过传输更长信号或将任务卸载至本地设备来满足用户期望QoS；用户需消耗能量对信号去噪，以恢复高保真潜在特征后送入语义解码器。为求解该问题，将其分解为参数优化和资源分配两个子问题：提出一种鲁棒编解码器，无需重新训练即可在多种压缩率和信道噪声下有效工作；采用贪心子载波分配降低通信时间。最后，在加性高斯白噪声信道下的标准图像数据集上进行仿真，结果表明，与仅速率自适应DeepJSCC或仅去噪方法相比，CoCo可降低总延迟，同时确保满足每个用户的需求。
英文摘要
Deep joint source-channel coding-enabled (DeepJSCC) semantic communication (SemCom) has excelled at delivering high perceptual quality at low channel-bandwidth ratios, which positions it as a pillar for next-generation wireless networks. However, the existing works have difficulty accommodating user heterogeneity in terms of communication channel quality, expected quality-of-service (QoS) targets, and the available local energy. Therefore, in this paper, we explicitly reflect the heterogeneity of user devices in terms of the differences in expected QoS, channel condition, and local energy, and then mathematically formulate the problem. Next, we propose an energy-aware compression-computation co-adaptation (CoCo) framework, in which the base station can meet the expected user QoS by transmitting a longer signal or offloading the task to a local device. The user has to dedicate energy to denoising the signal to recover higher-fidelity latent features before feeding it to the semantic decoder. To solve the formulated problem, we first decompose it into two sub-problems: parameter optimization and resource allocation problems. Specifically, we propose a robust codec that effectively works under a diversity of compression rates and channel noise without re-training, while the greedy sub-carrier allocation lowers the communication time. Finally, we present simulation results on standard image datasets over additive white Gaussian noise to demonstrate the effectiveness of CoCo, which reduces total latency relative to rate-only adaptive DeepJSCC or denoising-only, thereby ensuring the demands of each individual user are met.
Comments13 pages, 7 figures, 5 tables
