14 papers
Prediction-Powered Risk Monitoring of Deployed Models for Detecting Harmful Distribution Shifts
Guangyi Zhang, Yunlong Cai, Guanding Yu +1
We study the problem of monitoring model performance in dynamic environments where labeled data are limited. To this end, we propose prediction-powered risk monitoring (PPRM), a se…
Voxel-CKM: Voxelized Radio Frequency Radiance Fields for Fast and Few-Shot CKM Construction
Hanlei Li, Guangyi Zhang, Kequan Zhou +2
Channel knowledge maps (CKMs) are designed to predict channel state information (CSI) from user locations, thereby enabling low-overhead CSI acquisition. However, existing CKM cons…
Location-Agnostic Channel Knowledge Map Construction for Dynamic Scenes
Kequan Zhou, Guangyi Zhang, Hanlei Li +2
To alleviate the pilot and CSI-feedback burden in 6G, channel knowledge map (CKM) has emerged as a promising approach that predicts CSI solely from user locations. Nevertheless, ac…
Quantize-Sample-and-Verify: LLM Acceleration via Adaptive Edge-Cloud Speculative Decoding
Guangyi Zhang, Yunlong Cai, Guanding Yu +2
In edge-cloud speculative decoding (SD), edge devices equipped with small language models (SLMs) generate draft tokens that are verified by large language models (LLMs) in the clou…
F-CKM: Learning Channel Knowledge Map with Radio Frequency Radiance Field Rendering
Kequan Zhou, Guangyi Zhang, Hanlei Li +3
In 6G mobile communications, acquiring accurate and timely channel state information (CSI) becomes increasingly challenging due to the growing antenna array size and bandwidth. To…
Conformal Sparsification for Bandwidth-Efficient Edge-Cloud Speculative Decoding
Payel Bhattacharjee, Fengwei Tian, Meiyu Zhong +3
Edge-cloud speculative decoding (SD) accelerates inference by having a cloud-based large language model (LLM) that verifies draft tokens generated by a resource-constrained small l…