Sense Smarter, Think Better: Edge Perception for Next-Generation Networks

TL;DR

This survey comprehensively reviews edge perception in future wireless networks, focusing on sensing modalities, AI techniques, data fusion, and task-driven sensing for 6G, highlighting challenges and future directions.

Contribution

It systematically consolidates research on sensing, communication, and AI for edge perception, offering a structured overview and insights for 6G network design.

Findings

Analyzes various sensing modalities and AI techniques for edge perception.

Examines the integration of sensing, communication, and computation in edge systems.

Identifies key challenges and open issues in edge perception for 6G.

Abstract

Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.