Downlink training codebook design and hybrid precoding in FDD massive MIMO systems

Interest in massive multiple-input multiple-output (MIMO) systems is growing because of their potential ability to improve spectral and energy efficiency. Most prior work on massive MIMO considers TDD operation that relies on channel reciprocity between uplink and downlink channels, whereas most current cellular systems adopt FDD without channel reciprocity. In an FDD mode, downlink channel estimation becomes a challenging issue due to the substantial training overhead that scales with the number of antennas, which can limit the potential gain of massive MIMO systems. To tackle the issue of channel estimation, we consider the design of a training codebook that has a suitable mapping for the training signal patterns in block transmissions under the assumption of a Kaiman filtering framework. We focus on a reduced dimensionality training codebook and transmit precoding design to enable low-complexity system configuration. We discuss how this framework can extend to hybrid analog-digital precoding using a limited number of active RF chains for transmit beamforming by applying the Toeplitz distribution theorem to large-scale linear antenna arrays. A practical guideline for training codebook parameters is presented, and numerical results show the effectiveness of the proposed algorithm.