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Knowledge-Based Ultra-Low-Latency Semantic Communications for Robotic Edge Intelligence

Qunsong Zeng, Zhanwei Wang, You Zhou, Hai Wu, Lin Yang, Kaibin Huang

TL;DR

This work tackles ultra-low-latency, task-oriented robotic edge intelligence by leveraging a knowledge graph hosted at the edge as a remote brain. It introduces a knowledge-based SemCom protocol and an Ultra-Low-Latency Feature Transmission (ULL-FT) scheme that exploits the robustness of classifiers to compensate for high bit-error-rate uplink transmissions, enabling short-packet or uncoded uploads. The authors develop a Gaussian mixture model framework to relate BEP to classification margin, derive bounds on margin reduction under distortion, and propose margin-enhancement methods including multi-view aggregation and retransmissions. Experimental results with linear and deep CNN classifiers, using GM data and ModelNet40, demonstrate substantial latency reductions while preserving accurate feasible KP identification, validating the practical potential for robotic edge intelligence in 6G networks. The work points to promising future directions in KG modeling, semantic matching, multi-robot coordination, and integration with advanced physical-layer techniques.

Abstract

The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scale knowledge graph (KG) is operated at an edge server as a "remote brain" to guide remote robots on environmental exploration or task execution. In this paper, we present a new air-interface framework targeting the said systems, called knowledge-based robotic semantic communications (SemCom), which consists of a protocol and relevant transmission techniques. First, the proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task. They include identification of all task-relevant knowledge paths (KPs) on the KG, semantic matching between KG and object classifier, and uploading of robot's observations for objects recognition and feasible KP identification. Next, to support ultra-low-latency feature transmission (ULL-FT), we propose a novel transmission approach that exploits classifier's robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture model, we derive the relation between BEP and classification margin, which sheds light on system requirements to support ULL-FT. Furthermore, for the case where the classification margin is insufficient for coping with channel distortion, we enhance the ULL-FT approach by studying retransmission and multi-view classification for enlarging the margin and further quantifying corresponding requirements. Finally, experiments using DNNs as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible KP identification.

Knowledge-Based Ultra-Low-Latency Semantic Communications for Robotic Edge Intelligence

TL;DR

This work tackles ultra-low-latency, task-oriented robotic edge intelligence by leveraging a knowledge graph hosted at the edge as a remote brain. It introduces a knowledge-based SemCom protocol and an Ultra-Low-Latency Feature Transmission (ULL-FT) scheme that exploits the robustness of classifiers to compensate for high bit-error-rate uplink transmissions, enabling short-packet or uncoded uploads. The authors develop a Gaussian mixture model framework to relate BEP to classification margin, derive bounds on margin reduction under distortion, and propose margin-enhancement methods including multi-view aggregation and retransmissions. Experimental results with linear and deep CNN classifiers, using GM data and ModelNet40, demonstrate substantial latency reductions while preserving accurate feasible KP identification, validating the practical potential for robotic edge intelligence in 6G networks. The work points to promising future directions in KG modeling, semantic matching, multi-robot coordination, and integration with advanced physical-layer techniques.

Abstract

The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scale knowledge graph (KG) is operated at an edge server as a "remote brain" to guide remote robots on environmental exploration or task execution. In this paper, we present a new air-interface framework targeting the said systems, called knowledge-based robotic semantic communications (SemCom), which consists of a protocol and relevant transmission techniques. First, the proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task. They include identification of all task-relevant knowledge paths (KPs) on the KG, semantic matching between KG and object classifier, and uploading of robot's observations for objects recognition and feasible KP identification. Next, to support ultra-low-latency feature transmission (ULL-FT), we propose a novel transmission approach that exploits classifier's robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture model, we derive the relation between BEP and classification margin, which sheds light on system requirements to support ULL-FT. Furthermore, for the case where the classification margin is insufficient for coping with channel distortion, we enhance the ULL-FT approach by studying retransmission and multi-view classification for enlarging the margin and further quantifying corresponding requirements. Finally, experiments using DNNs as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible KP identification.
Paper Structure (39 sections, 7 theorems, 41 equations, 12 figures)

This paper contains 39 sections, 7 theorems, 41 equations, 12 figures.

Table of Contents

  1. Introduction
  2. Models and Metrics
  3. KP Finding
  4. Robot Observation Model
  5. Data Distribution
  6. Multi-view Data
  7. Communication Model
  8. Classifier Models and Metrics
  9. Linear Classifier
  10. CNN Classifier
  11. Multi-view Classification
  12. Robotic SemCom Protocol
  13. Ultra-Low-Latency Feature Transmission
  14. ULL-FT Scheme
  15. Channel Distortion
  16. ...and 24 more sections

Key Result

Lemma 1

Let $\{X_d\}$ be a sequence of square integrable i.i.d. random variables with $\mathbb{E}[X]=0$ and $\mathbb{E}[X^2]=1$. Consider a sequence $\{w_d\}$ and the following notations: If $W_D^2\to\infty$ and the condition $w_D^2/W_D^2=O(\frac{1}{D})$ holds, then where $Z$ is a standard normal random variable.

Figures (12)

  • Figure 1: Knowledge-based robotic SemCom system. The task of a robot, comprising a sequence of objects and actions, corresponds to paths within a KG. An edge server, equipped with KG, serves as a "remote brain" that identifies feasible KPs by analyzing the observations uploaded by the robot.
  • Figure 2: The workflow chart of the Robotic SemCom protocol.
  • Figure 3: An illustration of the operations in the proposed ULL-FT scheme. The robustness of classification is exploited to accommendation channel distortion.
  • Figure 4: Histogram of feature error $\Delta x_d$, i.e., the $d$-th dimension of feature vector $\mathbf{x}$, with zero value ($\Delta x_d=0$) removed. The BEP is set as 0.01.
  • Figure 5: The principle of coping with channel distortion using classification margin in GM model.
  • ...and 7 more figures

Theorems & Definitions (7)

  • Lemma 1: fisher1992skorohod Theorem 3.1
  • Lemma 2: gallego2013mahalanobis
  • Proposition 1
  • Lemma 3
  • Proposition 2
  • Proposition 3
  • Proposition 4