Hybrid Beamforming Considering Path Birth-death Process via Few-shot Learning

While data-driven approaches provide an effective solution for hybrid beamforming implementation, they currently fail to account for the path birth-death process (PBP), a phenomenon frequently encountered in practical environments. The occurrence of path birth-death process (PBP) leads to abrupt and significant shifts in the statistical properties of channel state information, which, in turn, necessitates frequent model updates to adjust to the altered channel conditions. Consequently, models generated by existing data-driven techniques that primarily focus on angle range variation are inadequate when path birth-death process (PBP) is present. In this paper, we propose a weight initialization strategy for hybrid beamforming model design, inspired by few-shot learning, to mitigate the challenges posed by path birth-death process (PBP). With this approach, the model can efficiently adapt to changes in the number of propagation channels by relying on starting weights and requiring only minimal online updates. Simulation outcomes show that the introduced technique greatly enhances adaptation efficiency compared to previous methods.