Resource Allocation for Transmit Hybrid Beamforming in Decoupled Millimeter Wave Multiuser-MIMO Downlink

Ahmed, Irfan; Khammari, Hédi; Shahid, Adnan

doi:10.1109/access.2016.2634096

Cited by 33 publications

(37 citation statements)

References 32 publications

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“…However, the high power consumption and the high cost of mixed-signal and RF chains (analog-to-digital converters (ADC)/digital-to-analog converters (DAC), data converters, mixers etc.) [18] led to opt for hybrid beamforming operating in the baseband and analog domains. Thus several studies have proposed different architectures aiming to reduce the number of RF chains by combining an analog RF beamformer and a baseband digital beamformer, such techniques are known as hybrid forming methods.…”

Section: A Hybrid Beamformingmentioning

confidence: 99%

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

Ahmed

Khammari

Shahid

et al. 2018

IEEE Commun. Surv. Tutorials

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633

297

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The increasing wireless data traffic demands have driven the need to explore suitable spectrum regions for meeting the projected requirements. In the light of this, millimeter wave (mmWave) communication has received considerable attention from the research community. Typically, in fifth generation (5G) wireless networks, mmWave massive multiple-input multipleoutput (MIMO) communications is realized by the hybrid transceivers which combine high dimensional analog phase shifters and power amplifiers with lower-dimensional digital signal processing units. This hybrid beamforming design reduces the cost and power consumption which is aligned with an energyefficient design vision of 5G. In this paper, we track the progress in hybrid beamforming for massive MIMO communications in the context of system models of the hybrid transceivers' structures, the digital and analog beamforming matrices with the possible antenna configuration scenarios and the hybrid beamforming in heterogeneous wireless networks (HetNET). We extend the scope of the discussion by including resource management issues in hybrid beamforming. We explore the suitability of hybrid beamforming methods, both, existing and proposed till first quarter of 2017, and identify the exciting future challenges in this domain.

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Section: A Hybrid Beamformingmentioning

confidence: 99%

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

Ahmed

Khammari

Shahid

et al. 2018

IEEE Commun. Surv. Tutorials

Self Cite

633

297

View full text Add to dashboard Cite

show abstract

“…For a given RF precoder F AB and the knowledge of perfect CSI at the base-station, the digital precoder can be obtained by conventional MU-MIMO techniques, e.g., the zero-forcing and block diagonalization [15].…”

Section: Relaxed-convex Transformationmentioning

confidence: 99%

“…Their work only focuses on the design of statistical CSI based analog precoder and user/beam selection. Our previous work [15] on resource allocation for transmit beamforming develops digital and analog precoders which maximize the sum rate with total power and desired number of RF chains constraints. The provided solutions require full instantaneous CSI at the transmitter and receiver, which, in case of the massive MIMO, consists of large number of pilot transmission in downlink and channel information feedback in the uplink.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], a reinforcement learning based framework for radio resource management in radio access networks has been proposed. In our previous work [22], we used neural networks to reduce the execution time of the computationally intensive resource allocation part of the joint resource allocation and hybrid beamforming design in [15]. However, in this work, we use K-mean based unsupervised machine learning scheme to group the users based on their spatial locations.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

et al. 2019

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In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users’ grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than 50 % feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by 27 . 6 % compared to the sum-rate of one-group case, and reduce the feedback overhead by 62 . 5 % compared to the full CSI feedback.

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“…It is known that spatial multiplexing is used to increase the spectral efficiency and diversity coding is used to increase the link reliability. In [3] and [4], hybrid precoding and QR methods are introduced respectively. SVD based…”

Section: Introductionmentioning

confidence: 99%

A Novel Iterative Detection in Downlink of Massive MIMO System

Sun¹,

Wang²,

Wang³

et al. 2019

JCC

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In this paper, we propose the novel iterative detection which operates iteratively on blocks of the received signal in downlink in massive MIMO (Massive Multiple-input Multiple-output) system. This article will combine the equivalent channel with soft detection and soft decoder, and finally propose a new structure for the iterative detection in downlink based on 5G simulation test platform of NCRL and analysis the performance of the novel structure. The simulation result shows iterative algorithm performs better than conventional detection with lower amount of iterations. Communications beamforming method to increase the capacity with optimum proper power allocation was proposed by [5] [6]. We use the two-level algorithm to decompose the multi-user MIMO channel into multiple single-user channels, which contains block diagonalization and SVD.Pilot contamination and interference are well-known problems affecting communication system, requiring the need of an equalizer. Various solutions to the problem were widely investigated. The optimal, iterative equalizer applying soft feedback is LMMSE ISDIC equalizer. Iterative channel estimation and data detection is researched in [7] [8]. We consider time division duplexing since it can exploit channel reciprocity [9]. An innovated double-turbo receiver was proposed to alleviate the pilot contamination and interference, which combines the equivalent channel estimation with the detector and the decoder.The paper is organized as follows. In Section 2, we propose the system model, including precoding algorithm, detection algorithm and so on. Section 3 introduces the novel detection structure and compares the proposed structure and the traditional one. Some numerical results with a performance comparison are shown in Section 4. Last, Section 5 draws the conclusions on the proposed schemes.

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Resource Allocation for Transmit Hybrid Beamforming in Decoupled Millimeter Wave Multiuser-MIMO Downlink

Cited by 33 publications

References 32 publications

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives

Spatio-Radio Resource Management and Hybrid Beamforming for Limited Feedback Massive MIMO Systems

A Novel Iterative Detection in Downlink of Massive MIMO System

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