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

2025-01-01

arXiv.org

AI Summary

AI-native receivers exhibit superior performance under high noise and reduced communication overhead, yet their robustness critically depends on the representativeness of training data—failing to generalize across real-world channel and waveform diversity. Method: We propose a post-deployment robustness assurance framework for AI-native base station transceivers. It employs a lightweight auxiliary neural network to parse 5G NR pilot signals in real time, jointly monitoring both out-of-distribution (OOD) channel shifts and output probability consistency between parallel AI and conventional receivers, thereby triggering on-demand, limited retraining. Contribution/Results: We introduce the first “measure–verify–recover” closed-loop mechanism that integrates OOD detection, cooperative inference, and lightweight adaptive retraining at the system level. Experiments show 99%, 97%, and 69% detection accuracy for channel profile, transmitter velocity, and delay spread shifts, respectively, with >86% timely retraining activation.

Technology Category

• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Ensuring AI-native receiver robustness in practical deployment conditions
• Detecting distribution shifts in wireless channel characteristics post-deployment
• Triggering timely retraining when performance degradation is identified

Innovation

• Continuous monitoring detects wireless channel distribution shifts
• Auxiliary neural network identifies out-of-distribution transmission parameters
• Reference receiver comparison triggers selective AI model retraining