Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications

Abstract

Millimetre wave (mmWave) is a promising technology to meet the ever-growing data traffic in the future. A major challenge of mmWave communications is the high path loss. In order to overcome this issue, mmWave systems often adopt beamforming techniques, which require robust channel estimation and beam tracking algorithms to maintain an adequate quality of service. This paper proposes a framework of channel estimation and beam tracking for mmWave communications. The proposed framework is designed for vehicular to infrastructure communication but can be extended to other applications as well. First, we propose a multi-stage adaptive channel estimation algorithm called robust adaptive multi-feedback (RAF). The algorithm is based on using the estimated channel coefficient to predict a lower bound for the required number of measurements. Our simulations demonstrate that compared with the existing algorithms, RAF can achieve the desired probability of estimation error (PEE), while on average reducing the feedback overhead by 75.5% and the total channel estimation time by 14%. Second, after estimating the channel in the first step, the paper follows by investigating the extended Kalman filter (EKF) for beam tracking in vehicular communications. A crucial part of EKF is the calculation of Jacobian matrices. We show that the model used in the previous work, which was based on the angles of arrival and departure, is not suitable for vehicular communications. This is due to the complexity in the calculation of Jacobian matrices. A new model is proposed for EKF in mmWave vehicular communications which is based on position, velocity and channel coefficient. Closed-form expressions are derived for the Jacobians used in EKF which facilitate the implementation of the EKF tracking algorithm in the proposed model. Finally, we provide an extensive number of simulations to substantiate the robustness of the framework as well as presenting the analytical results on the PEE of the RAF algorithm.Index Terms-Millimeter wave, multiple-input multiple-output (MIMO), channel estimation, beamforming, analog beamforming, beam tracking, Extended Kalman filter (EKF).

Channel Estimation Techniques for Millimeter-Wave Communication Systems: Achievements and Challenges

“…In order to establish a stable link between the BS and the MT, the channel estimation, and hence the beam alignment, must be completed or joined with a beam tracking scheme in order to track the variations in AoDs/AoAs at the receiver. Despite the efforts deployed in this area [205], [206], some extensive research is needed to develop practical and fast estimation/tracking algorithms for mmWave channels. • Quantization errors: In the literature, the beam steering directions are assumed to be quantized, i.e.…”

Particle Filters

“…There is a substantial number of studies in literature that have addressed beam tracking for millimeter wave systems [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Additionally, a number of articles deal exhaustively with beam tracking in free-space optical communications as well [15][16][17][18][19][20][21][22][23][24][25][26].…”

Learning-Based Predictive Beamforming for UAV Communications With Jittering

“…Having obtained the predicted angleθ k at the UE, it feeds back the predicted angle to the UAV via an out-of-band communication uplink. Relying on powerful channel coding techniques, the UAV can accurately acquire the predicted angle, as commonly adopted in [8]. Then the UAV and the UE can easily formulate their beamforming vectors for downlink communication at time k, i.e, w k = a θ k and f k = b θ k and establish the communication link 2 .…”

“…In the literature, the beam alignment problem has been intensively investigated for terrestrial mmWave massive MIMO systems. For instance, in [8], the authors proposed an extended Kalman filtering (EKF)based beam tracking scheme for mmWave systems. However, this approach is tailored for vehicular networks with only a slight angle variation, which their results are not be applicable to UAV communications with a wide range of angle fluctuations.…”