Massive multiple input multiple output (MIMO) antenna systems have received a great deal of interest due to their applicability to industrial network systems. One of the major obstacles that reducing… Click to show full abstract
Massive multiple input multiple output (MIMO) antenna systems have received a great deal of interest due to their applicability to industrial network systems. One of the major obstacles that reducing the performance of massive MIMO system is reference signal (RS) overhead which can be increased as the number of transmitter antenna increases in frequency division duplexing (FDD) system. Using channel reciprocity in time division duplexing (TDD) system can significantly increase the RS overhead performance using channel reciprocity. However, to use the channel reciprocity, channel calibration is a significant challenge to overcome in real system design. There are various base station (BS) calibration methods that have already successfully applied to the current BS, while there are few methods for the calibration of distributed user entities (UEs) and/or industrial Internet of things (IIoT) devices. In this paper, we propose a distributed UE RF calibration method of massive MIMO systems that uses the power headroom report. The power headroom is typically reported from the UEs and/or IIoT devices to the BS periodically or aperiodically. By including additional amplitude RF impairment information in the power headroom report, we can successfully transfer the necessary information from UE to BS. The proposed scheme does not require additional feedback, and is in compliance with current standards. There are five schemes based on the kind of information transferred to BS and the way of making a calibration factor that can be multiplied to the estimated channel. Numerical analysis shows that the proposed schemes can significantly increase the spectral efficiency with little system burden, and thus can be a core technology for the realization of massive MIMO for industrial network systems.
               
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