Training Sequence Design for Feedback Assisted Hybrid Beamforming in Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) communication is an emerging technology that uses an excess of transmit antennas to realize high spectral efficiency. Achieving potential gains with large-scale antenna arrays hinges on sufficient channel estimation accuracy. Much prior work focuses on time-division duplex (TDD)-based networks, relying on reciprocity between the uplink and downlink channels. However, most currently deployed commercial wireless systems are frequency-division duplex (FDD)-based, making it difficult to exploit channel reciprocity. In massive MIMO FDD systems, the problem of channel estimation becomes even more challenging due to the substantial training resources and feedback requirements which scale with the number of antennas. In this paper, we consider the problem of training sequence design and the mapping of training signals to training periods. We focus on reduced-dimension training sequence designs, along with transmit precoder designs, aimed at reducing both hardware complexity and power consumption. The resulting designs are extended to hybrid analog-digital beamforming systems, which employ a limited number of active RF chains for transmit precoding, by applying the Toeplitz distribution theorem to large-scale linear antenna systems. A practical guideline for training sequence parameter selection is presented along with performance analysis.