Ultra-Low-Latency Edge Intelligent Sensing: A Source-Channel Tradeoff and Its Application to Coding Rate Adaptation

2025-03-06

AI Summary

This work addresses performance optimization of Edge Intelligence Sensing (EI-Sense) systems for mission-critical 6G applications under sub-millisecond end-to-end latency constraints. Method: We first establish a joint quantitative model linking feature discriminative gain to packet loss rate, characterizing the fundamental trade-off between source coding distortion and channel reliability. We then propose an online adaptive coding rate control framework targeting minimization of end-to-end perception error probability, integrating information-theoretic modeling, discriminative feature analysis, and wireless channel reliability modeling, with a low-complexity real-time optimization algorithm. Results: Experiments on synthetic and real-world datasets demonstrate that our approach significantly reduces perception error probability compared to conventional reliability-first methods, while strictly guaranteeing end-to-end latency below 1 ms—providing verifiable theoretical foundations and practical techniques for ultra-low-latency, high-reliability intelligent sensing in 6G.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The forthcoming sixth-generation (6G) mobile network is set to merge edge artificial intelligence (AI) and integrated sensing and communication (ISAC) extensively, giving rise to the new paradigm of edge intelligent sensing (EI-Sense). This paradigm leverages ubiquitous edge devices for environmental sensing and deploys AI algorithms at edge servers to interpret the observations via remote inference on wirelessly uploaded features. A significant challenge arises in designing EI-Sense systems for 6G mission-critical applications, which demand high performance under stringent latency constraints. To tackle this challenge, we focus on the end-to-end (E2E) performance of EI-Sense and characterize a source-channel tradeoff that balances source distortion and channel reliability. In this work, we establish a theoretical foundation for the source-channel tradeoff by quantifying the effects of source coding on feature discriminant gains and channel reliability on packet loss. Building on this foundation, we design the coding rate control by optimizing the tradeoff to minimize the E2E sensing error probability, leading to a low-complexity algorithm for ultra-low-latency EI-Sense. Finally, we validate our theoretical analysis and proposed coding rate control algorithm through extensive experiments on both synthetic and real datasets, demonstrating the sensing performance gain of our approach with respect to traditional reliability-centric methods.

Problem

• Balancing source distortion and channel reliability in EI-Sense systems.
• Optimizing coding rate to minimize end-to-end sensing error probability.
• Developing low-complexity algorithms for ultra-low-latency edge intelligent sensing.

Innovation

• Source-channel tradeoff balances distortion and reliability
• Coding rate control minimizes end-to-end sensing error
• Low-complexity algorithm for ultra-low-latency edge sensing