Directional training for FDD massive MIMO

Xing Zhang, Lin Zhong, Ashutosh Sabharwal

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


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.

Original languageEnglish (US)
Article number8368089
Pages (from-to)5183-5197
Number of pages15
JournalIEEE Transactions on Wireless Communications
Issue number8
StatePublished - Aug 2018


  • Angle-of-arrival (departure)
  • Directional training
  • FDD
  • Massive MIMO

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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