This paper is concerned with the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we first propose a fast channel estimation (FCE) algorithm based on a novel overlapped beam pattern design, which can increase the amount of information carried by each channel measurement and thus reduce the required channel estimation time compared to the existing non-overlapped designs. We develop a maximum likelihood (ML) estimator to optimally extract the path information from the channel measurements. Then, we propose a novel rate-adaptive channel estimation (RACE) algorithm, which can dynamically adjust the number of channel measurements based on the expected probability of estimation error (PEE). The performance of both proposed algorithms is analyzed. For the FCE algorithm, an approximate closed-form expression for the PEE is derived. For the RACE algorithm, a lower bound for the minimum signal energy-to-noise ratio required for a given number of channel measurements is developed based on the Shannon-Hartley theorem. Simulation results show that the FCE algorithm significantly reduces the number of channel estimation measurements compared to the existing algorithms using non-overlapped beam patterns. By adopting the RACE algorithm, we can achieve up to a 6dB gain in signal energy-to-noise ratio for the same PEE compared to the existing algorithms.
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).
This paper is concerned with the channel estimation problem in millimetre wave (MMW) wireless systems with large antenna arrays. By exploiting the sparse nature of the MMW channel, we present an efficient estimation algorithm based on a novel overlapped beam pattern design. The performance of the algorithm is analyzed and an upper bound on the probability of channel estimation failure is derived. Results show that the algorithm can significantly reduce the number of required measurements in channel estimation (e.g., by 225% when a single overlap is used) when compared to the existing channel estimation algorithm based on non-overlapped beam patterns.
Abstract-In this paper, we consider the channel estimation problem in Millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent sparse nature of the mmWave channel, we develop a novel rate-adaptive channel estimation (RACE) algorithm, which can adaptively adjust the number of required channel measurements based on an expected probability of estimation error (PEE). To this end, we design a maximum likelihood (ML) estimator to optimally extract the path information and the associated probability of error from the increasing number of channel measurements. Based on the ML estimator, the algorithm is able to measure the channel using a variable number of beam patterns until the receiver believes that the estimated direction is correct. This is in contrast to the existing mmWave channel estimation algorithms, in which the number of measurements is typically fixed. Simulation results show that the proposed algorithm can significantly reduce the number of channel estimation measurements while still retaining a high level of accuracy, compared to existing multi-stage channel estimation algorithms.
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