On-the-fly Communication-and-Computing to Enable Representation Learning for Distributed Point Clouds

TL;DR

This paper introduces FlyCom$^2$, a framework for real-time distributed point cloud fusion in 6G networks, combining on-the-fly processing and communication-efficient aggregation to improve AI-enabled sensing applications.

Contribution

It proposes a novel on-the-fly communication and computing framework for distributed point cloud fusion, aligned with Gaussian process regression, optimizing local and global data integration.

Findings

Effective real-world dataset validation

Enhanced point cloud fusion accuracy

Balanced local processing and communication efficiency

Abstract

The advent of sixth-generation (6G) mobile networks introduces two groundbreaking capabilities: sensing and artificial intelligence (AI). Sensing leverages multi-modal sensors to capture real-time environmental data, while AI brings powerful models to the network edge, enabling intelligent Internet-of-Things (IoT) applications. These features converge in the Integrated Sensing and Edge AI (ISEA) paradigm, where edge devices collect and locally process sensor data before aggregating it centrally for AI tasks. Point clouds (PtClouds), generated by depth sensors, are crucial in this setup, supporting applications such as autonomous driving and mixed reality. However, the heavy computational load and communication demands of PtCloud fusion pose challenges. To address these, the FlyCom$^2$ framework is proposed, optimizing distributed PtCloud fusion through on-the-fly communication and computing, namely streaming on-sensor processing, progressive data uploading integrated communication-efficient AirComp, and the progressive output of a global PtCloud representation. FlyCom$^2$ distinguishes itself by aligning PtCloud fusion with Gaussian process regression (GPR), ensuring that global PtCloud representation progressively improves as more observations are received. Joint optimization of local observation synthesis and AirComp receiver settings is based on minimizing prediction error, balancing communication distortions, data heterogeneity, and temporal correlation. This framework enhances PtCloud fusion by balancing local processing demands with efficient central aggregation, paving the way for advanced 6G applications. Validation on real-world datasets demonstrates the efficacy of FlyCom$^2$, highlighting its potential in next-generation mobile networks.