  A near-field sensing (NISE) enabled predictive beamforming framework is
proposed to facilitate wireless communications with high-mobility channels.
Unlike conventional far-field sensing, which only captures the angle and the
radial velocity of the user, NISE enables the estimation of the full motion
state, including additional distance and transverse velocity information. Two
full-motion state sensing approaches are proposed based on the concepts of
estimation and tracking, respectively. 1)AGD-AO approach: The full motion state
of the user is estimated within a single CPI. In particular, the gradient
descent is adopted to estimate the transverse and radial velocities of the user
based on the maximum likelihood criteria, while the distance and the angle are
calculated by the kinematic model. In this process, moment estimations are
leveraged to adaptively tune the step size, thereby leading to a smoother and
faster gradient descent. 2) EKF approach: The full motion state of the user is
tracked across multiple CPIs. Based on the noisy measurements in multiple CPIs,
the EKF iteratively predicts and updates the current motion state to achieve a
low tracking error. Based on the obtained full motion state, the beam
prediction, and Doppler frequency compensation can be carried out with minimum
pilot overhead. Numerical results are provided to validate the effectiveness
and efficiency of the proposed approach compared to the conventional far-field
predictive beamforming and feedback-based approaches. It is also revealed that:
1)the proposed AGD-AO can achieve stable descending with small gradients,
thereby accelerating convergence; 2) compared to far-field predictive
beamforming and feedback-based schemes, both of the proposed methods exhibit
superior performance; and 3) by incorporating multiple CPIs, the EKF method
exhibits greater robustness in low SNR regions.
