← 研踪 PaperTrailarXiv:2611.14696 · v1
Risk-Aware Joint Inference and Resource Control for Physical AI in 6G Networks
首次提交 2026-11-11 04:01
版本更新 2026-11-11 04:01
雷达观测 2026-11-13 10:09
摘要
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 improvement in the average accuracy-latency-energy objective over fixed-high-SINR control, with near-zero constraint outage.
作者备注：Submitted to IEEE for possible publication
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v1 · 2026-11-11 04:01 · 最新观测版本
Risk-Aware Joint Inference and Resource Control for Physical AI in 6G Networks
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 improvement in the average accuracy-latency-energy objective over fixed-high-SINR control, with near-zero constraint outage.
查看此版本解读 →查看此版本原文 ↗