Abstract
Identifying the transmission status as line-of-sight (LOS) and non-line-of-sight (NLOS) is of importance for 3D Massive Multiple-Input Multiple-Output (MIMO) systems, which is one of the core technology to improve 5G New Radio (NR) capacity and spectral efficiency. If the identification could be as accurate as possible, the positioning systems and adaptive radio systems like cognitive radios can increase their performances significantly. This paper presents an improved algorithm for LOS/NLOS identification in 3D Massive MIMO systems. By fully considering the characteristics of the 3D MIMO channel, we formulate the identification problem as a binary hypothesis test by exploiting a statistic model based on time-space-frequency channel correlation. Compared to a previous study based on channel correlation, our simulation results show that the performance of the novel method is over 8.7% improved, and the error rate is as low as 3.23%. Besides, the effect of the number of antennas and taps in time domain on the performance of the improved method is discussed.
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