RACE: A Rate Adaptive Channel Estimation Approach for Millimeter Wave MIMO Systems

Abstract

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.

Fast Channel Estimation and Beam Tracking for Millimeter Wave Vehicular Communications

“…Finally, the impact of channel estimation error on tracking is investigated. Figure 5: Performance of the RAF algorithm compared to the algorithms in [17] and [10] in terms of PEE.…”

“…Therefore, devising an algorithm to ensure the PEE is crucial. The authors in [17], proposed a rate adaptive algorithm (RACE) in order to reach the desired PEE. Unfortunately, the algorithm requires a high number of channel feedback even for K = 2, particularly in low SNR.…”

“…Since the channel estimation algorithms have the same number of stages and follow the same process in each one, we compare the algorithms in a single stage which helps to reduce the simulation complexity. Moreover, in order to compare the channel estimation results to [10] and [17], we use K = 2, the maximum number of measurements of q max = 264 and the target PEE set to 10 −2 for all the algorithms.…”

“…5 represents the probability of estimation error for different SNRs of the communication system. The results are compared to two prior works [10] and [17]. In [10], the authors only considered the channel estimation without a limit for PEE.…”

“…As it can be seen in the figure, the algorithm cannot ensure that the probability of estimation error stays below the threshold of 10 −2 . Therefore, this algorithm is used as a baseline in this figure and the main comparison is between [17] and our work. The figure indicates that both algorithms achieve the desired PEE with a negligible difference.…”