A Blind Beam Tracking Scheme for Millimeter Wave Systems

Allied Power Constraint Optimization and Optimal Beam Tracking Schemes for Mobile mmWave Massive MIMO Communications

“…Thereafter, by following the results demonstration BiLMS proved to be the best compared to both LMS and EKF according to [21]. As shown in [22], a useful blind beam tracking approach with particle filter can be an effective scheme to address the challenges of excessive overhead due to consideration of more training sequences according to IEEE 802.11 ad and beam misalignment, respectively. Authors in [23] proposed an advanced beam tracking algorithm which does not require spatial scanning at the time of data transmission.…”

Markov Multi-Beamtracking on 60 GHz Mobile Channel Measurements

“…• MIMO architecture: Most papers consider either analog beamforming [20]- [27] where all antennas of a PAA are synthesized into a single DD beam to support a single digital chain in SISO (Single-Input Single-Output)or hybrid beamformingwhere subarrays of a PAA (or multiple PAAs) are synthesized into multiple beams to support multiple chains in SU (Single-User) or MU (Multiple User)-MIMO [28]- [37]. Yet one paper [38] considers full digital beamforming, where each PAA antenna supports a single chain in MIMO, even though it bears a significantly higher implementation cost vs. hybrid beamforming.…”

“…• 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.…”