In this thesis, we investigate user scheduling in large-scale MIMO downlink systems for next wireless communication systems. This thesis consists of three parts and its contents are as follows.
In the first part of the thesis, a new user-scheduling-and-beamforming method is proposed for multi-user massive multiple-input multiple-output (massive MIMO) broadcast channels in the context of two-stage beamforming. The key ideas of the proposed method are 1) to use a set of orthogonal reference beams and construct a double cone around each reference beam to select `nearly-optimal' semi-orthogonal users based only on channel quality indicator (CQI) feedback and 2) to apply post-user-selection beam refinement with zero-forcing beamforming (ZFBF) based on channel state information (CSI) feedback only from the selected users. It is proven that the proposed scheduling-and-beamforming method is asymptotically optimal as the number of users increases. Furthermore, the proposed scheduling-and-beamforming method almost achieves the performance of the existing semi-orthogonal user selection with ZFBF (SUS-ZFBF) that requires full CSI feedback from all users, with significantly reduced feedback overhead which is even less than that required by random beamforming.
In the second part of the thesis, the performance of random beamforming (RBF) and the multi-user (MU) gain in millimeter-wave (mm-wave) MU multiple-input single-output (MISO) downlink systems are analyzed based on the uniform random single-path (UR-SP) channel model suitable for highly directional mm-wave radio propagation channels. It is shown that under the UR-SP channelmodel, RBF achieves linear sum rate scaling with respect to (w.r.t.) the number of transmit antennas and furthermore yields optimal sum rate performance when the number of transmit antennas is large, if the number of users increases linearly w.r.t. the number of transmit antennas. Several beamtraining and user selection methods are investigated to yield insights into themost effective beamforming and scheduling choice for mm-wave MU-MISO in various operating conditions. Simulation results validate our analysis based on asymptotic techniques for finite cases.
In the last part of the thesis, the performance of RBF with partial channel state information feedback is investigated for mm-wave MU-MISO downlink systems under the uniform random multi-path (UR-MP) channel
model. In particular, the required number of users in the cell for RBF to yield linear sum rate scaling with respect to the number of transmit antennas is identified under the UR-MP channel model for different levels of channel sparsity. Then, the problem of user scheduling for MU-MISO downlink is considered when the number of users in the cell is not sufficiently large (sparse user regime) for the system to operate in the identified sufficient user regime. By exploiting the sparsity of mm-wave radio channels, user scheduling algorithms that implement maximal ratio transmit (MRT) beamforming or equal gain combining (EGC) transmit beamforming with reasonable amount of feedback are proposed for the sparse user regime. Our numerical results show that the proposed algorithms yield very good performance in sparse mm-wave channels.