Robust Group Sparse Beamforming for Dense C-RANs with Probabilistic SINR Constraints

Teng, Yinglei; Zhao, Wanxin

doi:10.1109/wcnc.2017.7925637

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…Moreover, the BD algorithm does not work well in the presence of imperfect CSI and can not remove inter-cluster interference totally. In order to enhance the performance of the proposed user clustering and downlink beamforming in the presence of imperfect CSI, one can use a more sophisticated similarity metric in the clustering algorithm or robust beamforming in the second stage of the proposed approach [49]- [51]. However, these considerations are beyond of the scope of this work.…”

Section: B Results and Discussionmentioning

confidence: 99%

Energy-Efficient User Clustering and Downlink Beamforming for MIMO-SCMA in C-RAN

Norouzi

Champagne

2021

IEEE Access

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Multiple-input multiple-output (MIMO) sparse code multiple access (SCMA) is of great interest for future wireless networks to achieve higher spectral efficiency and support massive connectivity. In this paper, we investigate the key problems of user clustering and downlink beamforming for MIMO-SCMA in a cloud radio access network (C-RAN). Using channel state information available at the central processor, an efficient user clustering algorithm based on the constrained K-means method is proposed. Subsequently, two iterative algorithms for beamforming design are developed by minimizing the total transmission power under quality-of-service (QoS) and fronthaul capacity constraints. In the first approach, we approximate the continuous non-convex constraints by convex conic ones using firstorder Taylor expansion and iteratively solve a sequence of mixed-integer second order cone programs (MI-SOCPs) to achieve high quality solution, but with higher complexity. In the second approach, a twostage low-complexity solution is developed in which beamforming matrices obtained from each stage are combined to form a single beamformer for each user. In the first stage, cluster beamformers are designed by taking advantage of block diagonalization, while in the second stage, user-specific beamformers are determined by minimizing transmission power. The performance of the proposed user clustering and downlink beamforming approaches for MIMO-SCMA in C-RAN is validated through simulations over mmWave channels. Compared to benchmark approaches, the results show significant improvements in terms of transmit power and spectral efficiency.INDEX TERMS MIMO-SCMA, mmWave, C-RAN, downlink beamforming, user clustering, constrained K-means.

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Section: B Results and Discussionmentioning

confidence: 99%

Energy-Efficient User Clustering and Downlink Beamforming for MIMO-SCMA in C-RAN

Norouzi

Champagne

2021

IEEE Access

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“…Next, we will convert the nonconvex expression in (11) to a deterministic convex constraint based on Bernstein-type inequality [14] [15].…”

Section: A Bernstein Approximation To the Chance Constraint Of Energmentioning

confidence: 99%

“…Next, we will convert the nonconvex expression in (11) to a deterministic convex constraint based on Bernstein-type inequality [14] [15]. Let M = c H Rc + 2Re c H P , where R ∈ H N is a complex Hermitian matrix, P ∈ C N , c ∼ CN (0, I), for any δ > 0,…”

Section: A Bernstein Approximation To the Chance Constraint Of Energy...mentioning

confidence: 99%

“…The BSs and users are distributed in a region of 800*800 independently, and the distribution of the user and the BSs follow the Homogeneous Poisson Point Process (HPPP). Taking into account large scale fading and smallscale fading, the channel parameters is presented in TABLE 1 [14]. In addition, we assume that the CSI error follows the independent complex Gauss distribution.…”

Section: Simulation and Analysismentioning

confidence: 99%

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Robust Beamforming for SWIPT System with Chance Constraints

Teng

Zhao

Yan

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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The robust beamforming problem in multiple-input single-output (MISO) downlink networks of simultaneous wireless information and power transfer (SWIPT) is studied in this paper. Adopting the time switching fashion to perform energy harvesting and information decoding respectively, we aim at maximizing the sum rate under imperfect channel state information (CSI) and the chance constraints of users' harvested energy. In view of the fact that the constraints for minimal harvested energy is not necessary to meet from time to time, this paper adopts chance constraint to model it and uses the Bernstein inequality to transform it into deterministic constraints equivalently. Recognizing the maximum sum rate problem of imperfect CSI as nonconvex problem, we transform it into finding the expectation of minimum mean square error (MMSE) equivalently in this paper, and an alternative optimization (AO) algorithm is proposed to decompose the optimization problem into two sub-problems: the transmit beamformer design and the division of switching time. The simulation results show the performance gains compared to non-robust state of the art schemes.

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Characterization of sparse beamforming for energy efficiency in cloud radio access networks using Gauss–Poisson process

Farahmand

Mohammadi

2018

Wireless Netw

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Robust Group Sparse Beamforming for Dense C-RANs with Probabilistic SINR Constraints

Cited by 9 publications

References 9 publications

Energy-Efficient User Clustering and Downlink Beamforming for MIMO-SCMA in C-RAN

Energy-Efficient User Clustering and Downlink Beamforming for MIMO-SCMA in C-RAN

Robust Beamforming for SWIPT System with Chance Constraints

Characterization of sparse beamforming for energy efficiency in cloud radio access networks using Gauss–Poisson process

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