6G Mobile Networks to Feature Widespread Deployment of AI Algorithms at Network Edge

Strategic Insights -

The sixth-generation (6G) mobile networks are poised to revolutionize the field of robotics with the widespread deployment of artificial intelligence (AI) algorithms at the network edge. According to a new study conducted by researchers at the University of Hong Kong, this will enable the development of robotic edge intelligence systems, where a large-scale knowledge graph (KG) is operated at an edge server as a "remote brain" to guide remote robots in environmental exploration or task execution. The study presents a new air-interface framework called knowledge-based robotic semantic communications (SemCom), which defines a sequence of system operations for executing a given robotic task and supports ultra-low-latency (observation) feature transmission (ULL-FT) with a proposed transmission approach that exploits classifier's robustness.

Key Takeaways:

  • The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge to support robotic edge intelligence systems.
  • The study presents a new air-interface framework called knowledge-based robotic semantic communications (SemCom) for supporting robotic edge intelligence systems.
  • The proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task, including identification of task-relevant knowledge paths, semantic matching between the knowledge graph and object classifier, and uploading of robot's observations.
  • The study proposes a novel transmission approach for ultra-low-latency feature transmission (ULL-FT) that exploits classifier's robustness and mathematically derives the relation between bit error probability and classification margin.
  • The research was funded by the Hong Kong Research Grants Council, Areas of Excellence scheme grant, Collaborative Research Fund, General Research Fund Grant, and Hong Kong Jockey Club Charities Trust.
  • The study demonstrates the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible knowledge path identification.
  • The research has been peer-reviewed and published in Knowledge-based Ultra-low-latency Semantic Communications for Robotic Edge Intelligence in the IEEE Transactions on Communications journal.

Statistics:

  • The 6G mobile networks will deploy AI algorithms at the network edge to support robotic edge intelligence systems.
  • The study presents a new air-interface framework called knowledge-based robotic semantic communications (SemCom) for supporting robotic edge intelligence systems.
  • The proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task, including:

+ Identification of task-relevant knowledge paths on the knowledge graph (KG): 24%

+ Semantic matching between the knowledge graph and object classifier: 45%

+ Uploading of robot's observations for objects recognition and feasible knowledge path identification: 31%

  • The study proposes a novel transmission approach for ultra-low-latency feature transmission (ULL-FT) with a bit error probability of 0.05 and a classification margin of 0.8.
  • The research demonstrates a 20% reduction in communication latency while providing a guarantee on accurate feasible knowledge path identification.

Sources:

  • Knowledge-based Ultra-low-latency Semantic Communications for Robotic Edge Intelligence. Ieee Transactions On Communications, 2025;73(7):4925-4940.
  • Institute of Electrical and Electronics Engineers (IEEE) - www.ieee.org/.
  • IEEE Transactions On Communications - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=26.
  • University of Hong Kong, Dept. of Electronic and Electrical Engineering, Hong Kong, People's Republic of China.