LAUSR

Massive multiple-input multiple-output (MIMO) attracts considerable interest by increasing the spectral efficiency, and which may be adopted in future communication system. Both base station (BS) and user equipment (UE) can be equipped with large-scale antennas using frequency division duplex (FDD) schemes. The major obstacle that reduces the performance is the overhead due to the reference signal. Moreover, the channel state information (CSI) of the uplink (UL) channel cannot be simply used for downlink (DL) pre-coding. In this paper, we focus on reconstructing wireless channels on a frequency band by observing the channel response on neighboring frequency band. The inference of DL channel is conducted by utilizing parameters of multipath components extracted from UL observations, using high resolution estimation algorithms, e.g. the Space-Alternating Generalized Expectation-maximization (SAGE). Four calibration methods are proposed to improve the quality of the inferred DL channel response. Those four methods take into account the channel composition based on the availability of the initial channel observations. Their performance is demonstrated by using real massive MIMO measurements. The results show these calibration methods have their individual application scenarios and all outperform in Line-of-Sight (LoS) scenarios. These calibration methods prove a methodological way to improve the correlation of neighboring channel.