Grouping Optimization Based Hybrid Beamforming for Multiuser MmWave Massive MIMO Systems

Ding, Yadi; Hu, Anzhong

doi:10.1109/ccet48361.2019.8989341

Cited by 4 publications

(6 citation statements)

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“…represents the kth channel matrix from the BS to the user, and h K ∈ ℂ N r ×1 is the channel vector of the kth user. n ∈ ℂ N r ×1 represents the complex Gaussian white noise vector, obeying C N ð0, σ 2 I N r Þ, F BB , W BB can be designed by the Minimum Mean Square Error (MMSE) criterion in [28,30], i.e.,…”

Section: Systemmentioning

confidence: 99%

“…From equations ( 4) and ( 5), we know that F BB and W BB can be obtained from F RF and H [28]. That is, the design of the hybrid beamforming matrix is converted into the design of F RF , i.e.,…”

Section: Problem Descriptionmentioning

confidence: 99%

“…3), it is known that in a multiuser mmWave massive MIMO system, the kth user will suffer interference from (k − 1)th users. Based on [28], the correlation between the user channel vectors is used to measure the interference strength. The correlation is represented by the matrix C ∈ ℝ K×K , i.e.,…”

Section: Problem Descriptionmentioning

confidence: 99%

“…These studies are well able to combat interference for multiuser transmission. However, for the design of hybrid beamforming, the hybrid beamforming algorithm proposed in [28] is based on the idea of grouping, and in a multiuser mmWave MIMO system, an exhaustive algorithm is used to search within the divided group, and an avarice algorithm is then used between the groups to select the optimal beam, uplink channel is used, and the complexity of the exhaustive search is still very high. In [29], on the other hand, an avarice iterative approach is used, where an element of the analog beamforming matrix is majorizated only once.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we propose an improved low-complexity hybrid beamforming grouping sum-rate maximization (HBG-SRM) algorithm based on [28] to address the above problem. This algorithm can effectively reduce interuser interference and maximize the multiuser massive MIMO system sum-rate.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Beamforming Grouping Sum-Rate Maximization Algorithm for Multiuser mmWave Massive MIMO Systems

Liu

Yang

Sun

et al. 2022

International Journal of Digital Multimedia Broadcasting

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For multiuser millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the key factor that causes the system sum-rate to change is its interference plus noise, corresponding to the number of base station (BS) antennas and signal-to-noise ratio (SNR) variation causing the system sum-rate changes, which in turn affects the user’s communication quality. Based on this, this paper proposes an improved low-complexity hybrid beamforming grouping sum-rate maximization (HBG-SRM) algorithm to achieve system sum-rate maximization under the premise that both BS and user side use hybrid beamforming architecture and the channel state information (CSI) of the downlink channel is perfect. The algorithm first predefines a relevant threshold value, which is used to group multiusers; then, the user uses the maximum likelihood (ML) criterion to identify the optimal beam and estimate its beamforming gain within each group, and finally, the user compares all the candidate optimal beam gains between each group to confirm the optimal beamforming vector. The simulation results also verify the superiority of the proposed algorithm’s sum-rate over other algorithms.

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Section: Systemmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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