Anomaly Detection-Based UE-Centric Inter-Cell Interference Suppression

TL;DR

This paper introduces a novel UE-centric interference suppression method using anomaly detection and interference whitening, significantly improving interference detection and mitigation in 5G systems with limited training resources.

Contribution

It proposes a new deep support vector data description-based anomaly detection scheme for inter-cell interference suppression, effective with limited training data and validated on commercial 5G hardware.

Findings

Outperforms baseline interference detection methods.

Achieves performance close to ideal interference suppression.

Demonstrates effectiveness across various 3GPP channel models.

Abstract

The increasing spectral reuse can cause significant performance degradation due to interference from neighboring cells. In such scenarios, developing effective interference suppression schemes is necessary to improve overall system performance. To tackle this issue, we propose a novel user equipment-centric interference suppression scheme, which effectively detects inter-cell interference (ICI) and subsequently applies interference whitening to mitigate ICI. The proposed scheme, named Z-refined deep support vector data description, exploits a one-class classification-based anomaly detection technique. Numerical results verify that the proposed scheme outperforms various baselines in terms of interference detection performance with limited time or frequency resources for training and is comparable to the performance based on an ideal genie-aided interference suppression scheme. Furthermore, we demonstrate through test equipment experiments using a commercial fifth-generation modem chipset that the proposed scheme shows performance improvements across various 3rd generation partnership project standard channel environments, including tapped delay line-A, -B, and -C models.