面向 6G 网络物理 AI 的风险感知联合推理与资源控制方法 RACE
Risk-Aware Joint Inference and Resource Control for Physical AI in 6G Networks
AI 导读
论文提出风险感知边缘推理控制框架 RACE，根据预测动作失败概率与任务关键度自适应设定 SINR 目标，并结合截止时间约束进行资源分配。该框架由慢时间尺度的大语言模型（LLM）提出任务需求供确定性验证，快速混合 actor 选择推理配置与发射功率。仿真显示其平均精度-时延-能耗目标较固定高 SINR 控制提升 11%，约束违规接近零。
正文 · 原文
Abstract:Wireless edge inference enables Physical-AI devices to execute edge-inferred actions using transmitted sensory inputs. Since the risk of incorrect actions vary across observations and tasks, the associated uplink transmission of sensory inputs requires different levels of wireless reliability. For instance, applying a fixed signal-to-interference-plus-noise ratio (SINR) requirement can overprovision low-risk tasks while underprovisioning high-risk ones. Therefore, we propose risk-aware control for edge inference (RACE), which maps predicted action-failure probability and task criticality to adaptive SINR targets and combines them with deadline-derived SINR requirements. A slow-timescale large language model (LLM) proposes task requirements for deterministic validation, while a fast hybrid actor selects inference configurations and transmit power levels, complemented by analytical receive beamforming and finite power restoration. Simulations show $11\%$ improvement in the average accuracy-latency-energy objective over fixed-high-SINR control, with near-zero constraint outage.
| Comments: | Submitted to IEEE for possible publication |
| Subjects: | Networking and Internet Architecture (cs.NI); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Signal Processing (eess.SP) |
| Cite as: | arXiv:2611.14696 [cs.NI] |
| (or arXiv:2611.14696v1 [cs.NI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14696 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Amin Farajzadeh [view email]
[v1]
Tue, 10 Nov 2026 20:01:15 UTC (266 KB)
来源：arXiv cs.AI · arxiv.org