Robust Large Scale Calibration for Massive MIMO

In the massive multiple-input multiple-output (MIMO) era, there are hundreds of antenna elements at one single base station (BS). It becomes critical to take full advantage of time-division duplexing (TDD) channel reciprocity to learn the downlink (DL) channel state information (CSI) from the uplink (UL) channel measurements at the BS. However, due to different radio frequency branches for transmitting and receiving, antenna calibration at the BS is necessary to effect this reciprocity between the end-to-end DL and UL channels. In massive MIMO, this procedure becomes extremely challenging since all the existing antenna calibration approaches do not scale well with the size of the antenna array. In this paper, we rely on compressive sensing (CS) theory to develop an efficient and robust way to calibrate the massive antenna array at the BS, which only requires CSI feedback in the order of O(log N) instead of O(N) as in those conventional calibration schemes. Extensive simulations demonstrate our approach can maintain the whole large scale antenna system in a "calibrated´´ state with only a small amount of feedback overhead. The proposed novel scheme thus allows us to make full use of the channel reciprocity in TDD massive MIMO systems.