Angle-Domain Frequency Synchronization for Massive MIMO Uplink with Adaptive MUI Suppression

Ge, Yinghao; Zhang, Weile; Gao, Feifei; Mu, Pengcheng; Zhao, Guangzhe

doi:10.1109/glocom.2018.8647596

Cited by 7 publications

(16 citation statements)

References 32 publications

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“…Unfortunately, the aforementioned methods are not resistant to multipath fading effects. In [17]- [20], a group of researchers investigated the CFO estimation problem in massive MIMO multiuser OFDM systems where multipath channel models are considered. In [17], a computationally efficient blind CFO estimator was presented for MIMO OFDM system where the minimization of cost function is computationally efficient.…”

Section: Introductionmentioning

confidence: 99%

“…Another CFO estimator for multi-user MIMO OFDM systems was proposed in [19], taking a joint spatial-frequency alignment procedure so that the CFO can be estimated for users separately. Later in [20], an angledomain adaptive filtering based CFO synchronization method was proposed. This method performs a two-stage multiuser interference (MUI) suppression so that the CFO of users with separate or overlapped angle-of-arrival regions can be estimated individually.…”

Section: Introductionmentioning

confidence: 99%

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Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility

et al. 2019

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Carrier frequency offset (CFO), which often occurs due to the mismatch between the local oscillators in transmitter and receiver, limits the performance of multiple-input multiple-output (MIMO) wireless communication systems. To recover the CFO, the first step is coarse CFO estimation. This paper presents a neural network (NN) based coarse CFO estimator which has higher compatibility with a variety of MIMO systems, comparing with traditional CFO estimators. Instead of performing closed form calculation as some traditional estimators do, the proposed estimator transforms the estimation problem to a classification problem: classify the optimal coarse CFO estimate from a pool of coarse CFO candidates. Taking the advantage of neural networks, the proposed NN estimator can perform coarse CFO estimations for MIMO systems with different numbers of antennas and a variety of channel models. Meanwhile, the testing results show that the proposed NN estimator has promising performance and wide CFO acquisition range. INDEX TERMS Coarse CFO estimation, MIMO, neural network, higher compatibility. XINMING HUANG (M'01-SM'09) received the Ph.D. degree in electrical engineering from Virginia Tech, in 2001. He was a Member of Technical Staffs with the Wireless Advanced Technology Laboratory, Bell Labs of Lucent Technologies. Since 2006, he has been a Faculty Member with the Department of Electrical and Computer Engineering, Worcester Polytechnic Institute (WPI), where he is currently a Full Professor. His main research interests include the areas of circuits and systems, with an emphasis on reconfigurable computing, wireless communications, information security, computer vision, and machine learning. ZHE FENG received the B.S. degree from Beijing Jiaotong University and the M.S. degree from the University of Michigan at Ann Arbor. He is currently pursuing the Ph.D. degree with the University of Colorado Boulder. His current research interests include wireless communications and software-defined radio. YOUJIAN LIU (S'98-M'01) received the Ph.D. degree in electrical engineering from The Ohio State University, in 2001. He joined the Department of Electrical and Computer Engineering,

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility

et al. 2019

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“…Another method targeting at addressing the multi-CFO estimation issue in the presence of overlapping AoA regions among users is the angle-domain adaptive filtering (ADAF) approach in [29], [30], which can achieve two-stage MUI suppression. For each user, the angle-constraining matrix (ACM) consisting of a set of selected match-filter (MF) beamformers constitutes the first stage to inherently handle the MUI from non-overlapping users; while the judiciously designed ADAF vectors accomplish the second stage and substantially relieve the severe interference from the overlapping users.…”

Section: Introductionmentioning

confidence: 99%

“…For each user, the angle-constraining matrix (ACM) consisting of a set of selected match-filter (MF) beamformers constitutes the first stage to inherently handle the MUI from non-overlapping users; while the judiciously designed ADAF vectors accomplish the second stage and substantially relieve the severe interference from the overlapping users. The ADAF approach [29], [30] not only preserves the merit of JSFA that both CFO estimation and data detection can be performed separately for each user, but also can be applied to scenarios of users with mutually overlapping AoA regions. However, it can only tolerate slight overlapping of AoA regions among different users.…”

Section: Introductionmentioning

confidence: 99%

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User-Coupling Angle-Domain Adaptive Filtering Based Frequency Synchronization for Massive MIMO Multiuser Uplink

Zhang

2019

IEEE Access

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In this paper, we develop a user-coupling angle-domain adaptive filtering (UC-ADAF)-based frequency synchronization method for the uplink of a massive multiple-input multiple-output (MIMO) multiuser network, in the presence of highly overlapping angle-of-arrival (AoA) regions among coexisting users. In order to deal with the extremely severe multiuser interference (MUI) among users with highly overlapping AoA region, we propose to couple them together and perform joint carrier frequency offset (CFO) estimation and data detection. For the coupled users, the UC angle-constraining matrix (UC-ACM) can inherently suppress the interference from non-overlapping users and the UC-ADAF vectors are adaptively optimized to further eliminate the MUI from slightly overlapping users. Moreover, the challenging problem of addressing the severe MUI caused by highly overlapping users is circumvented through joint estimation and detection. The effectiveness of the proposed UC-ADAF approach and its robustness against overlapping degree among coupled users and the total number of mutually overlapping users, as well as its superiority over the existing methods, are demonstrated by numerical results. INDEX TERMS Frequency synchronization, carrier frequency offset (CFO), user-coupling angle-domain adaptive filtering (UC-ADAF), multiuser interference (MUI) suppression, multiuser massive MIMO.

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RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

Mohammadian

Tellambura

2021

IEEE Access

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Wireless transceivers for mass-market applications must be cost effective. We may achieve this goal by deploying non-ideal low-cost radio frequency (RF) analog components. However, their imperfections may result in RF impairments, including phase noise (PN), carrier frequency offset (CFO), and in-phase (I) and quadrature-phase (Q) imbalance. These impairments introduce in-band and out-of-band interference terms and degrade the performance of wireless systems. In this survey, we present RF-impairment signal models and discuss their impacts. Moreover, we review RF-impairment estimation and compensation in singlecarrier (SC) and multicarrier systems, especially orthogonal frequency division multiplexing (OFDM). Furthermore, we discuss the effects of the RF impairments in already-established wireless technologies, e.g., multiple-input multiple-output (MIMO), massive MIMO, full-duplex, and millimeter-wave communications and review existing estimation and compensation algorithms. Finally, future research directions investigate the RF impairments in emerging technologies, including cell-free massive MIMO communications, nonorthogonal multicarrier systems, non-orthogonal multiple access (NOMA), ambient backscatter communications, and intelligent reflecting surface (IRS)-assisted communications. Furthermore, we discuss artificial intelligence (AI) approaches for developing estimation and compensation algorithms for RF impairments.

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Angle-Domain Frequency Synchronization for Massive MIMO Uplink with Adaptive MUI Suppression

Cited by 7 publications

References 32 publications

Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility

Coarse Frequency Offset Estimation in MIMO Systems Using Neural Networks: A Solution With Higher Compatibility

User-Coupling Angle-Domain Adaptive Filtering Based Frequency Synchronization for Massive MIMO Multiuser Uplink

RF Impairments in Wireless Transceivers: Phase Noise, CFO, and IQ Imbalance – A Survey

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