Fig. 1. 3GPP 5G NR beam management protocol
Operating at millimeter-wave (mmWave) frequencies introduces new challenges in path loss, blockage, and signal propagation, and beam steering is a key technology to overcome these issues. 5G NR specifies new initial access procedures to ensure alignment of the directional transmissions used in beam steering. 5G NR Release 15 specifies new procedures for initial access and attach when establishing the wireless link connection. Since neither the device nor the base station knows the other’s location, the base station uses beam sweeping to transmit channel information in sync blocks across the spectrum. The UE determines the strongest match and transmits back to the base station. Once the base station knows the direction of the UE, it establishes a communication link.
Fig. 2. Flow chart: Location-aware beam alignment with search window 
Beam alignment is required in millimeter-wave communication to ensure high data rate transmission. However, with narrow beamwidth in massive MIMO, beam alignment could be computationally intensive due to the large number of beam pairs to be measured. To target this problem, we’ve studied the location-aware beam alignment framework by exploiting the location information of the user equipment (UE) and potential reflecting points . Both UE and BS perform a coordinated beam search from a small set of beams within the error boundary of the location information. The selected beams are then used to guide the search for future beams. To further reduce the number of beams to be searched, we propose an intelligent search scheme within a small window of beams to determine the direction of the actual beam. The BS and UE coordinate the search iteratively and the search is stopped when the target rate is achieved.
In mmWave mobile systems, the beam tracking is required to maintain the beam alignment. Two common tracking methods for mmWave beamforming are codebook-based method and perturbation method. However, the codebook method is limited by the resolution of the codebook whereas perturbation method requires lengthy beam training which incurs large overhead as well as delay, rendering these approaches unsuitable for mobile environment. To solve this problem, we’ve studied the filter-based beam tracking which estimates the channel parameters (channel gain and angle) and error compensation method which control the beamwidth adaptively. In , the BS tracks the UE based on particle filter and, the adequate beamwidth is set via the partial activation of the antenna array. In , the channel is tracked with the extended Kalman filter (EKF) at the BS and the beamforming weight is updated with a robust minimum mean squared error beamformer bounded by the array vector error which is fed from the error variance estimated by the EKF.
Fig. 3. Particle filter beam tracing with beamwidth adaptation  (left) and EKF beam tracking with robust MMSE robust beamforming  (right)
 Orikumhi, J. Kang, C. Park, J. Yang and S. Kim, "Location-Aware Coordinated Beam Alignment in mmWave Communication," in Proc. IEEE Allerton 2018, Monticello, USA, Oct. 2018.
 H. Chung, J. Kang, H. Kim, Y. M. Park, and S. Kim, “Adaptive Beamwidth Control for mmWave Beam Tracking,” accepted to IEEE Commun. Lett., Sep. 2020.
 S. Jayaprakasam, X. Ma, J.W. Choi, and S. Kim. “Robust Beam-tracking for mmWave Mobile Communications,” IEEE Commun. Lett., vol. 21, no. 12, pp. 2654-2657, Dec. 2017.