Large-scale MIMO beamforming using successive channel state estimation and codebook extension

In this paper, we propose channel training and beamforming schemes to maximize the benefit of large-scale antenna, or massive multiple-input multiple-output (MIMO) systems. The proposed schemes are operated by transmitting continuous precoding matrix indicator (PMI) feedback signals through a series of pattern. Conventional communication systems, such as LTE systems, use PMI, rank indicator (RI), and channel quality indicator (CQI) to report the channel state information (CSI) to the enhanced node B (eNB), with an increasing feedback overhead as the number of antenna elements increases. The proposed beamforming and feedback schemes solve this problem by iterative CSI reports and codebook extension. Each PMI report is conducted based on a component codebook of smaller size, and cumulated feedback information is utilized to build an extended beamformer. The proposed methods are applicable to many different types of wireless systems, including frequency division duplex (FDD) systems which do not possess the channel reciprocity assumed by many of earlier works. The methods also ensure the backward compatibility which allows the operation of legacy user equipment (UE). The continuous feedback process also enables the enhancement of the codebook resolution. The resulting system performance improvement is verified by computer simulation results.