Two-Phase Beam Selection for 5G MIMO Systems

The beam training process is divided into two distinct phases: a beam sweep phase that identifies candidate beams using omni-based sweeping, and a beam refinement phase that selects optimal beams from candidates. This segmentation allows the system to achieve accurate beam selection without performing exhaustive training, thereby reducing overall overhead while maintaining selection accuracy.

The beam sweep phase performs preliminary action by identifying and selecting candidate beams before the refinement phase. By pre-filtering the beam space and identifying promising candidates in advance, the refinement phase only needs to compare a reduced set of candidates rather than evaluating all possible beams, thus reducing total training overhead while maintaining accuracy.

The reception complexity is segmented into two phases: during the beam sweep phase, the receiver performs simple omni-based measurements to identify candidates without complex processing. During the refinement phase, only the selected candidates undergo detailed evaluation. This segmentation maintains reliability by evaluating multiple candidates while reducing overall reception complexity through phased processing.

The system employs feedback mechanisms where the receiver reports beam measurement results back to the transmitter. During the beam sweep phase, feedback identifies candidate beams. During refinement, feedback continues to guide the selection process. This feedback loop enables the system to efficiently narrow down candidates and maintain robustness without requiring the receiver to continuously process all possible beam configurations, thus reducing complexity.