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

Xu Chen, Hai Wu, Kaibin Huang

TL;DR

The paper addresses the challenge of heavy computation and high communication load in distributed PtCloud fusion for ISEA by introducing FlyCom$^2$, a framework that streams lightweight on-sensor processing, uses AirComp for efficient aggregation, and leverages Gaussian process regression to progressively learn a global PtCloud representation. It provides a principled end-to-end design that jointly optimizes local observation matrices and AirComp receivers under temporal correlations, with theoretical and empirical validation on real PtCloud datasets. Key contributions include a progressive GP-based fusion protocol, complexity analyses, three case studies (noiseless, one-shot AirComp, and on-the-fly progressive fusion), and a termination rule based on convergence of the observation-dimensionality. The results show that FlyCom$^2$ can achieve near-Oracle rendering while substantially reducing data uploading, highlighting its potential for next-generation 6G sensing and edge AI applications.

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.

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

TL;DR

The paper addresses the challenge of heavy computation and high communication load in distributed PtCloud fusion for ISEA by introducing FlyCom, a framework that streams lightweight on-sensor processing, uses AirComp for efficient aggregation, and leverages Gaussian process regression to progressively learn a global PtCloud representation. It provides a principled end-to-end design that jointly optimizes local observation matrices and AirComp receivers under temporal correlations, with theoretical and empirical validation on real PtCloud datasets. Key contributions include a progressive GP-based fusion protocol, complexity analyses, three case studies (noiseless, one-shot AirComp, and on-the-fly progressive fusion), and a termination rule based on convergence of the observation-dimensionality. The results show that FlyCom can achieve near-Oracle rendering while substantially reducing data uploading, highlighting its potential for next-generation 6G sensing and edge AI applications.

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 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 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, highlighting its potential in next-generation mobile networks.
Paper Structure (36 sections, 5 theorems, 65 equations, 10 figures, 1 table)

This paper contains 36 sections, 5 theorems, 65 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. System Model and Metrics
  3. Distributed PtCloud Sensing
  4. PtCloud Voxelization and Occupancy Feedback
  5. Distributed PtCloud Fusion via Representation Learning
  6. Communication Modelling
  7. Performance Metrics
  8. FlyCom$^2$-Based Distributed PtCloud Fusion
  9. Preliminaries of Gaussian Process Regression
  10. Overview of the Proposed FlyCom$^2$ Protocol
  11. On-the-Fly Observation Synthesis at Sensors
  12. AirComp-Based Observation Aggregation
  13. On-the-Fly Global Regression at a Server
  14. Discussions on Computation Complexity
  15. Complexity at Devices
  16. ...and 21 more sections

Key Result

Lemma 1

The mean function in eq:GPR_mean is the best linear unbiased predictor of $f(\mathbf{s})$ that achieves the minimal MSE that equals to $\mathsf{Var}[f(\mathbf{s})]$ in eq:GPR_var.

Figures (10)

  • Figure 1: An ISEA system for environmental perception in the context of (a) autonomous driving [https://researchleap.com/research-in-autonomous-driving-a-historic-bibliometric-view-of-the-research-development-in-autonomous-driving/] and (b) mixed reality [https://ieeexplore-ieee-org.eproxy.lib.hku.hk/stamp/stamp.jsp?tp=&arnumber=5980567], supported by (c) the proposed FlyCom$^2$ framework.
  • Figure 2: Illustration of octree-based search of spatial occupancy in a 2D space (quadtree), where octants in 3D space reduce into cubes and each cube will have $4$ child cubes with their occupancy being indicated by $4$ bits. In the figure, orange triangles are PtCloud points. The tree nodes are composed of filled (bit $"1"$) or unfilled (bit $"0"$) green circles indicating occupied or unoccupied cubes respectively. The generated bit sequence from depth $d=1$ to depth $d=3$ is $1\ 1110\ 0111\ 0111\ 1101$.
  • Figure 3: Factor graph of FlyCom$^2$-based distributed PtCloud fusion featuring parallel streams of on-device observation synthesis, AirComp-based aggregation, and global Gaussian process regression.
  • Figure 4: Generalized eigenvalues obtained through GED in \ref{['eq:opt_solution_flycom']} for the progressive FlyCom$^2$ operations. Data samples are selected from depth-$5$ octree nodes. Each sensor will process $N=24$ bins. Other experimental settings are the same as those discussed in Section \ref{['section:experiments']}.
  • Figure 5: Illustration of Microsoft Voxelized Upper Bodies and their distributed PtClouds.
  • ...and 5 more figures

Theorems & Definitions (8)

  • Lemma 1: MSE GPR_predictor2010
  • Remark 1
  • Lemma 2
  • Lemma 3
  • Remark 2: Balanced Tradeoff Between Data Heterogeneity and Channel Diversity
  • Lemma 4
  • Remark 3: Temporal Correlation in FlyCom$^2$
  • Proposition 1: Termination Rule