Tensor-Based Hybrid Precoding Processor for 8 × 8 × 8 mmWave 3D-MIMO Systems

Hybrid baseband precoding and RF beamforming is a highly efficient technology for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Threedimensional (3D) MIMO system with uniform planar array (UPA) of transit antennas and uniform linear array (ULA) of receive antennas can provide more flexible and efficient beamforming capability in sparse mmWave channels. Tensor is a compact multi-way algebraic model that can describe high-dimension systems such as the sparse mmWave 3D-MIMO system. This paper proposes a tensor-based hybrid precoding algorithm for continuous time-drifting 3D-MIMO systems which can achieves better performance in high bit-stream and low SNR systems. The FPGA implementation of the tensor-based hybrid precoding processor can support the 3D-MIMO system with 8 × 8 UPA transmitter and 8-antenna ULA receiver with a maximal normalized throughput of 17.0 M matrices/sec compared to the existing counterparts.