Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162605
Title: MMV-based sequential AoA and AoD estimation for millimeter wave MIMO channels
Authors: Zhang, Wei
Dong, Miaomiao
Kim, Taejoon
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Zhang, W., Dong, M. & Kim, T. (2022). MMV-based sequential AoA and AoD estimation for millimeter wave MIMO channels. IEEE Transactions On Communications, 70(6), 4063-4077. https://dx.doi.org/10.1109/TCOMM.2022.3168886
Journal: IEEE Transactions on Communications
Abstract: The fact that the millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channel has sparse support in the spatial domain has motivated recent compressed sensing (CS)-based mmWave channel estimation methods, where the angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using angle dictionary matrices. However, the existing CS-based methods usually obtain the estimation result through one-stage channel sounding that have two limitations: (i) the requirement of large-dimensional dictionary and (ii) unresolvable quantization error. These two drawbacks are irreconcilable; improvement of the one implies deterioration of the other. To address these challenges, we propose, in this paper, a two-stage method to estimate the AoAs and AoDs of mmWave channels. In the proposed method, the channel estimation task is divided into two stages, Stage I and Stage II. Specifically, in Stage I, the AoAs are estimated by solving a multiple measurement vectors (MMV) problem. In Stage II, based on the estimated AoAs, the receive sounders are designed to estimate AoDs. The dimension of the angle dictionary in each stage can be reduced, which in turn reduces the computational complexity substantially. We then analyze the successful recovery probability (SRP) of the proposed method, revealing the superiority of the proposed framework over the existing one-stage CS-based methods. We further enhance the reconstruction performance by performing resource allocation between the two stages. We also overcome the unresolvable quantization error issue present in the prior techniques by applying the atomic norm minimization method to each stage of the proposed two-stage approach. The simulation results illustrate the substantially improved performance with low complexity of the proposed two-stage method.
URI: https://hdl.handle.net/10356/162605
ISSN: 0090-6778
DOI: 10.1109/TCOMM.2022.3168886
Rights: © 2022 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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