Directional Training for FDD Massive MIMO

Abstract

A key challenge for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) is the large overhead in acquiring channel state information (CSI) for transmits beamforming. In this paper, we propose a scalable method called directional training to obtain downlink CSI. Directional training is motivated by two empirical results derived from massive MIMO channel measurements. First, the number of dominant angle-of-arrivals (departures) is much smaller than and nearly independent of the number of base-station antennas. Second, there is a strong correlation between uplink arrival and downlink departure angles even in FDD systems, which leads to the idea of directional training, where a small number of training symbols can be sent to estimate the dominant components of the downlink channel. Therefore, directional training measures much fewer complex coefficients than full-training-based methods, and as a result, compared with full-training, the overall channel acquisition overhead for directional training scales much slower with the number of base-station antennas. We evaluate directional training with extensive experiments with a 64-antenna base-station at two bands separated by approximately 72 MHz. Our results show that directional training-based downlink beamforming outperforms full-training systems by 150% in terms of average spectral efficiency, and loses only 5.3% average spectral efficiency from genie-aided systems.