Joint Optimization of Hybrid Beamforming for Multi-User Massive MIMO Downlink

Li, Zheda; Han, Shengqian; Sangodoyin, Seun; Wang, Rui; Molisch, Andreas F.

doi:10.1109/twc.2018.2808523

Cited by 62 publications

(30 citation statements)

References 40 publications

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“…The comparison of performance of ML-based users grouping with previous work cannot be provided because there is no previous work that uses ML-based technique to reduce the CSI feedback overhead in massive MIMO systems. Many papers use users grouping in massive MIMO hybrid beamforming [3,5,39,40], but they do not utilize ML-based users grouping. Therefore, we have compared our proposed solution with two benchmarks of full-CSI (G = K) and coarse-CSI (G = 1).…”

Section: Simulation Resultsmentioning

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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Section: Simulation Resultsmentioning

confidence: 99%

“…Till now, different variants of the JSDM have been proposed. Li et al [5] generalize the JSDM scheme to support non-orthogonal virtual sectorization and with multiple RF chains at both link ends. It uses the Kronecker channel model to decouple the transmit and receive beamforming.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…The detailed simulation configurations are listed in Table III. Simulation results in the same environment are used in [19], [20].…”

Section: A the Data Setmentioning

confidence: 99%

Band Assignment in Dual Band Systems: A Learning-Based Approach

Burghal

Wang

Molisch

2018

MILCOM 2018 - 2018 IEEE Military Communications Conference (MILCOM)

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We consider the band assignment problem in dual band systems, where the base-station (BS) chooses one of the two available frequency bands (centimeter-wave and millimeterwave bands) to communicate data to the mobile station (MS). While the millimeter-wave band offers higher data rate when it is available, there is a significant probability of outage during which the communication should be carried on the centimeterwave band.In this work, we use a machine learning framework to provide an efficient and practical solution to the band assignment problem. In particular, the BS trains a Neural Network (NN) to predict the right band assignment decision using observed channel information. We study the performance of the NN in two environments: (i) A stochastic channel model with correlated bands, and (ii) microcellular outdoor channels obtained by simulations with a commercial ray-tracer. For the former case, for sake of comparison we also develop a threshold based band assignment that relies on the optimal mean square error estimator of the best band. In addition, we study the performance of the NN-based solution with different NN structures and different observed parameters (position, field strength, etc.). We compare the achieved performance to linear and logistic regression based solutions as well as the threshold based solution. Under practical constraints, the learning based band assignment shows competitive or superior performance in both environments.

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“…When fully digital precoding architecture is employed in a massive multiple-input multiple-output (MIMO) system, the associated hardware costs are high [1][2][3], as each antenna requires a dedicated radio-frequency (RF) chain. Hybrid precoding is one of the solutions proposed in the literature to reduce the hardware cost in massive MIMO systems [4][5][6][7], where fewer RF chains are employed as when compared to the fully digital precoding architecture. In the hybrid precoding, analog phase shifters are used for the analog precoding and are connected with the digital precoding through a limited number of RF chains.…”

Section: Introductionmentioning

confidence: 99%

Interference Exploitation-Based Hybrid Precoding With Robustness Against Channel Errors

Fan

Hegde

Masouros

et al. 2020

2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)

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The extremely high cost associated with massive multipleinput multiple-output (MIMO) systems when it is employed with fully digital precoding can be reduced by applying hybrid precoding at an expense of increased transmit power. In such a hybrid precoding system, the transmit power required to achieve a certain quality-of-service (QoS) can be significantly reduced by employing the constructive interference (CI) precoding technique. However, as illustrated in the paper, the symbol error rate (SER) performance of CI-based precoding is very sensitive to channel errors. To address this challenge we propose a hybrid precoding approach with robustness against channel quantization error and channel estimation error. Simulation results demonstrate the superior energy efficiency of the proposed robust hybrid precoding when compared to that of a conventional non-robust precoding scheme in achieving a required QoS target.

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Joint Optimization of Hybrid Beamforming for Multi-User Massive MIMO Downlink

Cited by 62 publications

References 40 publications

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

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

Band Assignment in Dual Band Systems: A Learning-Based Approach

Interference Exploitation-Based Hybrid Precoding With Robustness Against Channel Errors

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