The 6G mobile networks are differentiated from 5G by two new usage scenarios - distributed sensing and edge AI. Their natural integration, termed integrated sensing and edge AI (ISEA), promised to create a platform for enabling environment perception to make intelligent decisions and take real-time actions. A basic operation in ISEA is for a fusion center to acquire and fuse features of spatial sensing data distributed at many agents. To overcome its communication bottleneck due to multiple access by numerous agents over hostile wireless channels, we propose a novel framework, called Spatial Over-the-Air Fusion (Spatial AirFusion), which exploits radio waveform superposition to aggregate spatially sparse features over the air. The technology is more sophisticated than conventional Over-the-Air Computing (AirComp) as it supports simultaneous aggregation over multiple voxels, which partition the 3D sensing region, and across multiple subcarriers. Its efficiency and robustness are derived from exploitation of both spatial feature sparsity and multiuser channel diversity to intelligently pair voxel-level aggregation tasks and subcarriers to maximize the minimum receive SNR among voxels under instantaneous power constraints. To optimally solve the mixed-integer Voxel-Carrier Pairing and Power Allocation (VoCa-PPA) problem, the proposed approach hinges on two useful results: (1) deriving the optimal power allocation as a closed-form function of voxel-carrier pairing and (2) discovering a useful property of VoCa-PPA that dramatically reduces the solution-space dimensionality. Both a low-complexity greedy algorithm and an optimal tree-search based approach are designed for VoCa-PPA. Extensive simulations using real datasets show that Spatial AirFusion achieves significant error reduction and accuracy improvement compared with conventional AirComp without awareness of spatial sparsity.

翻译：第六代（6G）移动网络与第五代（5G）相比，其区别性特征在于两大新型应用场景：分布式感知与边缘人工智能。两者的自然融合被称为通感算一体化（ISEA），有望构建一个能够感知环境、做出智能决策并实施实时行动的平台。在ISEA中，一个基本操作是融合中心获取并融合分布在多个智能体上的空间感知数据特征。为克服海量智能体通过恶劣无线信道多址接入带来的通信瓶颈，我们提出一种名为"空间空中融合（Spatial AirFusion）"的新框架。该框架利用无线电波形叠加原理，在空域中聚合空间稀疏特征。该技术比传统空中计算（AirComp）更为复杂，因为它支持跨多个体素（划分三维感知区域的基本单元）以及跨多个子载波的同时聚合。其高效性与鲁棒性源于同时利用空间特征稀疏性与多用户信道分集，通过智能配对体素级聚合任务与子载波，在瞬时功率约束下最大化各体素的最小接收信噪比。为最优求解混合整数"体素-载波配对与功率分配（VoCa-PPA）"问题，所提方法依赖于两个关键结论：（1）推导出最优功率分配作为体素-载波配对的闭式函数；（2）发现VoCa-PPA的有用性质，可显著降低解空间维度。针对VoCa-PPA问题，我们分别设计了低复杂度的贪心算法与基于最优树搜索的方法。基于真实数据集的广泛仿真表明，与未利用空间稀疏性的传统AirComp相比，Spatial AirFusion实现了显著的误差降低与精度提升。