VERITAS: Verifying the Performance of AI-native Transceiver Actions in Base-Stations

TL;DR

VERITAS is a framework that monitors AI-native transceivers in real-time, detects environmental shifts, and triggers retraining to maintain robust communication performance in dynamic wireless conditions.

Contribution

The paper introduces VERITAS, a novel joint measurement-recovery framework that detects distribution shifts and initiates retraining of AI-native transceivers in practical deployments.

Findings

VERITAS detects channel profile changes with 99% accuracy.

It identifies transmitter speed variations with 97% accuracy.

Retraining is successfully triggered in over 86% of cases when shifts are detected.

Abstract

Artificial Intelligence (AI)-native receivers prove significant performance improvement in high noise regimes and can potentially reduce communication overhead compared to the traditional receiver. However, their performance highly depends on the representativeness of the training dataset. A major issue is the uncertainty of whether the training dataset covers all test environments and waveform configurations, and thus, whether the trained model is robust in practical deployment conditions. To this end, we propose a joint measurement-recovery framework for AI-native transceivers post deployment, called VERITAS, that continuously looks for distribution shifts in the received signals and triggers finite re-training spurts. VERITAS monitors the wireless channel using 5G pilots fed to an auxiliary neural network that detects out-of-distribution channel profile, transmitter speed, and delay spread. As soon as such a change is detected, a traditional (reference) receiver is activated, which runs for a period of time in parallel to the AI-native receiver. Finally, VERTIAS compares the bit probabilities of the AI-native and the reference receivers for the same received data inputs, and decides whether or not a retraining process needs to be initiated. Our evaluations reveal that VERITAS can detect changes in the channel profile, transmitter speed, and delay spread with 99%, 97%, and 69% accuracies, respectively, followed by timely initiation of retraining for 86%, 93.3%, and 94.8% of inputs in channel profile, transmitter speed, and delay spread test sets, respectively.