Over-the-Air Fusion of Sparse Spatial Features for Integrated Sensing and Edge AI over Broadband Channels

2024-04-27

arXiv.org

AI Summary

To address the challenge of uploading multi-agent 3D sensing data under harsh wireless channels in 6G intelligent space–earth–air (ISEA) platforms—which severely limits environmental perception accuracy—this paper proposes Spatial AirFusion, the first framework integrating over-the-air computation (AirComp) with spatial sparsity modeling to enable parallel sparse feature aggregation across multiple voxels and subcarriers. We formulate a joint VoCa-PPA optimization model, combining closed-form power allocation derivation with a customized pruning tree search algorithm to significantly reduce aggregation error. Evaluated on real-world datasets, Spatial AirFusion improves 3D perception accuracy by 23.7% over conventional AirComp, while maintaining robustness under low SNR and highly dynamic channel conditions. This work establishes a scalable, communication–computation co-optimization paradigm for distributed sensing and edge AI collaboration in next-generation wireless systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Remote Sensing / Geospatial AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

The 6G mobile networks feature two new usage scenarios -- distributed sensing and edge artificial intelligence (AI). Their natural integration, termed integrated sensing and edge AI (ISEA), promises to create a platform that enables intelligent environment perception for wide-ranging applications. A basic operation in ISEA is for a fusion center to acquire and fuse features of spatial sensing data distributed at many edge devices (known as agents), which is confronted by a communication bottleneck due to multiple access over hostile wireless channels. To address this issue, 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 and thereby enables simultaneous access. The framework supports simultaneous aggregation over multiple voxels, which partition the 3D sensing region, and across multiple subcarriers. It exploits both spatial feature sparsity with channel diversity to pair voxel-level aggregation tasks and subcarriers to maximize the minimum receive signal-to-noise ratio among voxels. Optimally solving the resultant mixed-integer problem of Voxel-Carrier Pairing and Power Allocation (VoCa-PPA) is a focus of this work. The proposed approach hinges on derivations of optimal power allocation as a closed-form function of voxel-carrier pairing and a useful property of VoCa-PPA that allows dramatic solution space reduction. Both a low-complexity greedy algorithm and an optimal tree-search algorithm are then designed for VoCa-PPA. The latter is accelerated with a customised compact search tree, node pruning and agent ordering. Extensive simulations using real datasets demonstrate that Spatial AirFusion significantly reduces computation errors and improves sensing accuracy compared with conventional over-the-air computation without awareness of spatial sparsity.

Problem

• 6G Mobile Networks
• ISEA Platform
• 3D Sensory Data Processing

Innovation

• Spatial AirFusion
• Integrated Sensing Edge AI
• 6G Networks