Performance Monitoring-Enabled Reliable AI-Based CSI Feedback

Abstract

Artificial intelligence (AI) has emerged as a promising tool in channel state information (CSI) feedback tasks. Although current research primarily focuses on improving feedback accuracy through innovative AI approaches, the reliability of these systems in real-world scenarios often goes overlooked. Specifically, a closer examination of the feedback accuracy of individual CSI samples reveals significant variations, underscoring the imperative need for performance monitoring of AI-based CSI feedback. Building upon this observation, we introduce a pragmatic framework for AI-based CSI feedback. This process involves assessing feedback accuracy (i.e., conducting performance monitoring) on the user side before transmitting the CSI codeword. In particular, this method utilizes a lightweight proxy decoder, trained via knowledge distillation, to emulate the mapping function of the original decoder at the base station. The goal is to generate, at the user end, CSI identical to that produced at the base station by the original, more powerful decoder, thus enable precise prediction of feedback accuracy. Simulation results demonstrate that our proposed performance monitoring method can precisely predict feedback accuracy with low complexity and accurately detect low-quality feedback samples with a detection rate of nearly 95%, ensuring reliable transmission.