Tiled Beamspace Processing for Scaling mmWave Massive MU-MIMO

Recent progress in millimeter wave (mmWave) silicon technologies has given rise to a new possibility: digital beamforming for truly massive multiuser (MU-) MIMO. However, there are two key challenges in scaling: packaging a vast array of antennas with the corresponding radio frequency integrated circuits (RFICs), and controlling the complexity of digital signal processing (DSP) for MU detection. In this paper, we show that modular tiled architectures, which simplify the task of RF packaging, also enable significant reduction of the communication and computational burden of DSP for MU-MIMO by utilizing beamspace techniques that take advantage of the sparsity of the mmWave channel. Specifically, we propose and investigate Linear Minimum Mean Squared Error (LMMSE) adaptive MU detection via novel tiled beamspace architectures in which the bulk of the DSP occurs in-place at each tile. The dimensionality reduction and parallelization enabled by such architectures not only reduce the computational burden of inference and training relative to a traditional "full array" baseline, but also significantly reduce the length of the required training period. We consider three different training strategies with differing requirements for computation and inter-tile communication: independent training for each tile, coordinated training across tiles, and hierarchical training based on independent training as a first stage. Simulation results show that these approaches can actually outperform the full array baseline when we limit the length of the training period.