The vision of 6G networks aims to enable edge inference by leveraging ubiquitously deployed artificial intelligence (AI) models, facilitating intelligent environmental perception for a wide range of applications. A critical operation in edge inference is for an edge node (EN) to aggregate multi-view sensory features extracted by distributed agents, thereby boosting perception accuracy. Over-the-air computing (AirComp) emerges as a promising technique for rapid feature aggregation by exploiting the waveform superposition property of analog-modulated signals, which is, however, incompatible with existing digital communication systems. Meanwhile, hybrid reconfigurable intelligent surface (RIS), a novel RIS architecture capable of simultaneous signal amplification and reflection, exhibits potential for enhancing AirComp. Therefore, this paper proposes a Hybrid RIS-aided Digital AirComp (HRD-AirComp) scheme, which employs vector quantization to map high-dimensional features into discrete codewords that are digitally modulated into symbols for wireless transmission. By judiciously adjusting the AirComp transceivers and hybrid RIS reflection to control signal superposition across agents, the EN can estimate the aggregated features from the received signals. To endow HRD-AirComp with a task-oriented design principle, we derive a surrogate function for inference accuracy that characterizes the impact of feature quantization and over-the-air aggregation. Based on this surrogate, we formulate an optimization problem targeting inference accuracy maximization, and develop an efficient algorithm to jointly optimize the quantization bit allocation, agent transmission coefficients, EN receiving beamforming, and hybrid RIS reflection beamforming. Experimental results demonstrate that the proposed HRD-AirComp outperforms baselines in terms of both inference accuracy and uncertainty.

翻译：6G网络的愿景旨在通过利用广泛部署的人工智能（AI）模型实现边缘推理，为广泛的应用场景提供智能环境感知能力。边缘推理中的关键操作是边缘节点（EN）聚合由分布式智能体提取的多视角感知特征，从而提升感知精度。空中计算（AirComp）作为一种利用模拟调制信号波形叠加特性实现快速特征聚合的有前景技术应运而生，然而该技术与现有数字通信系统存在兼容性问题。与此同时，混合可重构智能表面（RIS）——一种能够同时实现信号放大与反射的新型RIS架构——展现出增强AirComp性能的潜力。为此，本文提出一种混合RIS辅助的数字AirComp（HRD-AirComp）方案，该方案采用矢量量化将高维特征映射为离散码字，再通过数字调制转换为符号进行无线传输。通过协同调整AirComp收发器与混合RIS反射配置以控制多智能体间的信号叠加，EN可从接收信号中估计聚合后的特征。为使HRD-AirComp具备面向任务的设计准则，我们推导了表征特征量化与空中聚合对推理精度影响的代理函数。基于此代理函数，我们构建了以最大化推理精度为目标的优化问题，并提出一种高效算法以联合优化量化比特分配、智能体传输系数、EN接收波束成形及混合RIS反射波束成形。实验结果表明，所提出的HRD-AirComp方案在推理精度与不确定性度量方面均优于基线方法。