Data-Free Knowledge Distillation for LiDAR-Aided Beam Tracking in MmWave Systems (opens in new tab)

We propose a data-free knowledge distillation (DF- KD) framework for LiDAR-aided mmWave beam tracking, where the objective is to predict the optimal current and future beams from a sequence of past LiDAR measurements. Specifically, we propose a knowledge inversion approach where a generator synthesizes LiDAR-like sequences from random noise, using a metadata loss to align the teachers internal feature statistics of synthetic and real data, w...