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上行链路毫米波MIMO-NOMA系统的频谱效率优化

Spectral Efficiency Optimization of Uplink Millimeter Wave MIMO-NOMA Systems.

作者信息

Zhang Yinhao, Deng Honggui, He Jun, Zhu Zaoxing, Peng Chengzuo, Xiao Haoqi

机构信息

School of Physics and Electronics, Central South University, Changsha 410083, China.

出版信息

Sensors (Basel). 2022 Aug 27;22(17):6466. doi: 10.3390/s22176466.

DOI:10.3390/s22176466
PMID:36080925
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9460619/
Abstract

In this paper, we considered uplink communication, focusing on the improvement of spectral efficiency (SE) for millimeter wave (mmWave) multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems. Firstly, we proposed an adaptive cluster head selection algorithm. Then, a channel-aligned analog beamforming scheme was designed based on the selected cluster heads. After that, the user grouping algorithm was designed based on the user-equivalent channel correlation. Subsequently, the power allocation problem was transformed from a nonconvex problem to a convex one using the quadratic transformation (QT) method considering all relevant constraints. Finally, the optimal user power allocation and digital beamforming design was obtained by iteratively optimizing the power and digital beamforming. Simulation results show that our proposed scheme can achieve a higher SE than existing methods.

摘要

在本文中,我们考虑了上行链路通信,重点关注毫米波(mmWave)多输入多输出非正交多址接入(MIMO-NOMA)系统的频谱效率(SE)提升。首先,我们提出了一种自适应簇头选择算法。然后,基于所选簇头设计了一种信道对齐模拟波束成形方案。之后,基于用户等效信道相关性设计了用户分组算法。随后,使用考虑所有相关约束的二次变换(QT)方法将功率分配问题从非凸问题转化为凸问题。最后,通过迭代优化功率和数字波束成形获得了最优用户功率分配和数字波束成形设计。仿真结果表明,我们提出的方案能够比现有方法实现更高的频谱效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/6bae424279a5/sensors-22-06466-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/ad6d20c41480/sensors-22-06466-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/aad75bb9b007/sensors-22-06466-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/4262e9bceb63/sensors-22-06466-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/8b79bd97ec70/sensors-22-06466-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/fee433184d5b/sensors-22-06466-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/bdf716a0d04e/sensors-22-06466-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/4dc9933f8a07/sensors-22-06466-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/6bae424279a5/sensors-22-06466-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/ad6d20c41480/sensors-22-06466-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/aad75bb9b007/sensors-22-06466-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/4262e9bceb63/sensors-22-06466-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/8b79bd97ec70/sensors-22-06466-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/fee433184d5b/sensors-22-06466-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/bdf716a0d04e/sensors-22-06466-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/4dc9933f8a07/sensors-22-06466-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1490/9460619/6bae424279a5/sensors-22-06466-g008.jpg

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