Yijian Chen

Field trial and future enhancements for TDD massive MIMO networks

Massive MIMO is one of the promising techniques to improve the spectral efficiency and network performance in future 5G networks. Compared to FDD, it is relatively easier to realize downlink massive MIMO for TDD as downlink channel information can be obtained via uplink-downlink channel reciprocity. This paper provides our field test results of massive MIMO system with a base station prototype equipped with 64 transmit antennas. Significant throughput gain is observed by performing 3D-beamforming to current LTE-Advanced handsets by using standard-transparent Multiuser(MU) MIMO techniques. With the massive MIMO base station prototype, MU-MIMO is realized by multiplexing maximum of eight handsets in spatial domain considering both azimuth and elevation directions. In addition to the field trial test results, future potential enhancements for TDD massive MIMO system are discussed. Evaluation results of evaluating some enhancements on uplink reference signal are provided.

High-Rank MIMO Precoding for Future LTE-Advanced Pro

We study the precoding for the high-rank MIMO in the LTE-A Pro systems. Unlike the low-rank precoding, layer mapping has a relatively large impact on the performance for high-rank precoding. First, we construct a model on the relationship between layer mapping and precoding. Then we derive the system throughput with the resulting model, so that the performance of the layer mapping and precoding can be evaluated. Further, we operate a sub-space optimization for the codebook-based precoding to maximize the system throughput. Simulation results show that after optimizing layer mapping, high-rank precoding achieves much better performance.

CSI feedback for massive MIMO system with dual-polarized antennas

Massive MIMO is a promising technique to provide high data rate with good energy efficiency for the future wireless cellular communication. However, its performance benefit often can be realized only when accurate channel state information (CSI) is available at the transmitter to perform accurate beamforming. With large number of antennas, full CSI consumes too much overhead to feed back without compression. To reduce CSI feedback overhead, CSI feedback scheme with dual stage precoding structure is designed to quantize the long term spatial channel correlation information and short term linear precoder information. In this paper, we discuss how to optimize this dual stage precoding scheme in the typical dual-polarized massive MIMO system. The eigenvalues of spatial correlation matrix are used to improve feedback efficiency. By relaxing the constant modulus constraint in codebook design, more flexible long term precoding can be used and adapt to the channel. A specific structure of long term precoding matrix for dual-polarized MIMO system is proposed to ensure the orthogonality of the final precoder for multi-layer transmission.