CQI-Based Interference Prediction for Link Adaptation in Industrial Sub-networks

TL;DR

This paper introduces a CQI-based interference prediction scheme that enhances link adaptation in industrial sub-networks by accurately modeling and estimating interference, even with impairments, to meet high-reliability low-latency requirements.

Contribution

It presents a novel interference prediction method using a vector discrete-time state-space model combined with a robust Student-t process regression, improving accuracy and reducing complexity in industrial network environments.

Findings

Achieves over 10x lower complexity than non-parametric baselines.

Maintains BLER below 1e-6 target in dense industrial settings.

Performs comparably to state-of-the-art supervised techniques using only CQI reports.

Abstract

We propose a novel interference prediction scheme to improve link adaptation (LA) in densely deployed industrial sub-networks (SNs) with high-reliability and low-latency communication (HRLLC) requirements. The proposed method aims to improve the LA framework by predicting and leveraging the heavy-tailed interference probability density function (pdf). Interference is modeled as a latent vector of available channel quality indicator (CQI), using a vector discrete-time state-space model (vDSSM) at the SN controller, where the CQI is subjected to compression, quantization, and delay-induced errors. To robustly estimate interference power values under these impairments, we employ a low-complexity, outlier-robust, sparse Student-t process regression (SPTPR) method. This is integrated into a modified unscented Kalman filter, which recursively refines predicted interference using CQI, enabling accurate estimation and compensating protocol feedback delays, crucial for accurate LA. Numerical results show that the proposed method achieves over 10x lower complexity compared to a similar non-parametric baseline. It also maintains a BLER below the 90th percentile target of 1e-6 while delivering performance comparable to a state-of-the-art supervised technique using only CQI reports.