Sequential Anomaly Detection Against Demodulation Reference Signal Spoofing in 5G NR

TL;DR

This paper introduces a sequential anomaly detection method leveraging the spatial sparsity of 5G channels to identify DMRS spoofing, significantly improving detection accuracy over existing techniques.

Contribution

The paper proposes a novel sequential sparsity-based detection approach for DMRS spoofing in 5G NR, exploiting the impact of spoofing on channel sparsity structure.

Findings

Outperforms subspace dimension based detection methods

Outperforms energy detector based detection methods

Effective in simulated 3GPP channel models

Abstract

In fifth generation (5G) new radio (NR), the demodulation reference signal (DMRS) is employed for channel estimation as part of coherent demodulation of the physical uplink shared channel. However, DMRS spoofing poses a serious threat to 5G NR since inaccurate channel estimation will severely degrade the decoding performance. In this correspondence, we propose to exploit the spatial sparsity structure of the channel to detect the DMRS spoofing, which is motivated by the fact that the spatial sparsity structure of the channel will be significantly impacted if the DMRS spoofing happens. We first extract the spatial sparsity structure of the channel by solving a sparse feature retrieval problem, then propose a sequential sparsity structure anomaly detection method to detect DMRS spoofing. In simulation experiments, we exploit clustered delay line based channel model from 3GPP standards for verifications. Numerical results show that our method outperforms both the subspace dimension based and energy detector based methods.