AI-in-the-Loop Sensing and Communication Joint Design for Edge Intelligence

2025-02-14

AI Summary

To address three critical bottlenecks in edge intelligence—low communication efficiency, high perceptual redundancy, and weak model generalization—this paper proposes an AI-in-the-loop framework unifying perception and communication. First, it establishes an explicit analytical relationship between validation loss and tunable system parameters (e.g., transmit power, batch size, sampling rate). Second, it embeds the learning model into a closed-loop control system to enable dynamic co-optimization of perception, communication, and computation. Third, it introduces gradient-importance-based sampling and a Stochastic Gradient Langevin Dynamics (SGLD)-inspired joint optimization of power and batch size. Experiments demonstrate 77% reduction in communication energy consumption, 52% decrease in perceptual sampling volume, and up to 58% reduction in validation loss—substantially enhancing model generalization. The core innovation lies in designing a differentiable, controllable, and end-to-end optimizable AI-driven closed-loop system architecture.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Recent breakthroughs in artificial intelligence (AI), wireless communications, and sensing technologies have accelerated the evolution of edge intelligence. However, conventional systems still grapple with issues such as low communication efficiency, redundant data acquisition, and poor model generalization. To overcome these challenges, we propose an innovative framework that enhances edge intelligence through AI-in-the-loop joint sensing and communication (JSAC). This framework features an AI-driven closed-loop control architecture that jointly optimizes system resources, thereby delivering superior system-level performance. A key contribution of our work is establishing an explicit relationship between validation loss and the system's tunable parameters. This insight enables dynamic reduction of the generalization error through AI-driven closed-loop control. Specifically, for sensing control, we introduce an adaptive data collection strategy based on gradient importance sampling, allowing edge devices to autonomously decide when to terminate data acquisition and how to allocate sample weights based on real-time model feedback. For communication control, drawing inspiration from stochastic gradient Langevin dynamics (SGLD), our joint optimization of transmission power and batch size converts channel and data noise into gradient perturbations that help mitigate overfitting. Experimental evaluations demonstrate that our framework reduces communication energy consumption by up to 77 percent and sensing costs measured by the number of collected samples by up to 52 percent while significantly improving model generalization -- with up to 58 percent reductions of the final validation loss. It validates that the proposed scheme can harvest the mutual benefit of AI and JSAC systems by incorporating the model itself into the control loop of the system.

Problem

• Enhances edge intelligence through AI-driven control
• Optimizes communication and sensing resource allocation
• Reduces energy consumption and improves model generalization

Innovation

• AI-in-the-loop joint sensing and communication
• Adaptive data collection via gradient importance
• Joint optimization of transmission and batch size