AI & ML
Characterizing AI Service Traffic for 6G: An Open Testbed and Initial Results from 3GPP SA4
arXiv cs.NI0 views
arXiv:2611.07250v1 Announce Type: new Abstract: The design of sixth generation (6G) mobile systems requires measurements of artificial intelligence (AI) service traffic under controlled network conditions. The 3rd Generation Partnership Project (3GPP) Service and System Aspects Working Group 4 (SA4) documents such measurements in draft Technical Report 26.870. In this paper, we describe the open testbed used for the study and analyze the results recorded in version 0.6.2 and its companion workbook. The testbed combines network emulation, packet capture, application measurements, and scenario execution against hosted and self-hosted models. The reported uplink to downlink volume ratios span more than six orders of magnitude. A personal assistant agent has a ratio of 0.007 at its local control interface and 0.50 on its wide-area egress. Deterministic agent signaling follows the configured round-trip delay. In a tokenized video experiment with 125 questions per profile, the number of correct answers changes by at most three through 10% independent uplink loss. Larger losses reduce accuracy, but the available repetitions do not establish a tolerance threshold. We also analyze emulator behavior that affects the interpretation of the study. Per-packet jitter introduces reordering, correlated random-loss settings suppress rare loss events, and a Gilbert-Elliott parameter mapping changes the stationary loss rate. The results support characterization by scenario, measurement layer, and versioned network configuration.
Source:https://arxiv.org/abs/2611.07250PreviousPRB Starvation at Slot Time via Rank Indicator Inflation: Attacking Proportional Fair Memory in the SchedulerNextDeep reinforcement learning-based optimization of multi-RAT handovers
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