.png){kind=icon}
.png){kind=icon}
Return
Edge AI Inference in 6G Mobile Networks: From Communication-Efficient Techniques to Task-Oriented Optimization
.png){kind=icon}
PRE
AI
Y
P
C
DOI：10.1109/COMST.2026.3690731.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The rapid proliferation of edge computing resources, together with transformative breakthroughs in artificial intelligence (AI), has catalyzed the emergence of edge AI. As a representative paradigm of AI-communication integration in 6G, edge AI extends AI capabilities to edge nodes and mobile devices, enabling ubiquitous, real-time, and secure intelligent services. Edge AI inference refers to the routine invocation phase of trained models, where forward propagation is executed to produce task-specific outputs under stringent latency and reliability requirements. However, it encounters critical challenges stemming from the escalating computational demands of AI models and the intrinsic resource limitations of edge environments. To address these challenges, this paper presents a comprehensive survey on edge AI inference in 6G mobile networks, identifying key principles and innovative solutions for optimizing inference performance under edge resource constraints. We begin by outlining communication-efficient techniques and adaptive model deployment strategies designed to mitigate transmission, computation, and storage overhead in edge AI inference. We then review collaborative inference mechanisms that facilitate effective workload distribution across cloud-edge-device computing nodes. This is followed by an in-depth exploration of task-oriented optimization, which coordinates network resources throughout the end-to-end inference workflow to boost the performance of both discriminative and generative edge AI inference. Finally, we examine practical platforms and promising applications, discuss future research directions and open issues, with the hope of inspiring continued advancements in this evolving field.
Keywords:
Edge AI inference
6G
cloud-edge-device computing
foundation models
task-oriented optimization
Journal
I
IF:
0
Papers:
67
Citations:
0
Organization
Cited Papers