Beam Tracking Particle Filter for Hybrid Beamforming and Precoding Systems

Markov Multi-Beamtracking on 60 GHz Mobile Channel Measurements

“…• number of hypotheses: Most papers commit to a single hypothesis per time [20]- [26], [28]- [32], [34][35], meaning that only information about the DD beam that is estimated per chain is propagated over time, whereas some papers propagate information about multiple candidate DD beams per chain, either through particle filtering [27][33] [36] or Bayesian inference [37][38], and so are more robust to harsh, uncertain, or rapidly changing channel conditions.…”

“…• tracking mechanism: Many papers assume an underlying motion modeltypically lineareither directly for the DD angle of the dominant paths or vis-à-vis R's motion, and solve a least-squares fit between the angles predicted from the motion and the angles within the codebook, either through Kalman filtering [21][23] [30][34], gradient descent [24][29] [31], or particle filtering [27][33] [36]. Others, rather, localize the scan within the codebook by means of a probability distributionuniform [22][28], Gaussian [24][32] [37], or exponential [26][38]centered at a previously estimated angle; the codebook angles are then conditioned by the probability when generating a new estimate, e.g.…”

“…[38]. Other channel models are quasi-deterministic [27] [36], which combine a statistical model with raytracing such that spatial consistency is inherent, whereas other papers employ raytracing only [32]. Most papers assume a sparse channel, i.e.…”