  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.
