低轨卫星大规模MIMO通信系统自适应混合波束赋形方法研究

鲜永菊; 黄小龙; 邢智童; 李云

doi:10.11999/JEIT260458

低轨卫星大规模MIMO通信系统自适应混合波束赋形方法研究

doi: 10.11999/JEIT260458 cstr: 32379.14.JEIT260458

鲜永菊^1,,
黄小龙¹,
邢智童^1, ,,
李云^{1, 2}

1.
重庆邮电大学通信与信息工程学院重庆 400065
2.
重庆邮电大学计算机科学与技术学院重庆 400065

基金项目: 国家自然科学基金(62301100)，重庆市教委科学技术研究计划项目青年项目(KJQN202200606, KJQN202300638)，重庆市自然科学基金(CSTB2024NSCQ-MSX0210, CSTB2024NSCQ-QCXMX0063, CSTC2024YCJH-BGZXM003)，重庆市自然科学基金创新发展联合基金(CSTB2025NSCQ-LZX0053)

详细信息

作者简介:
鲜永菊：女，副教授，研究方向为卫星通信、车联网和边缘计算，邮箱　xianyj@cqupt.edu.cn

黄小龙：男，硕士生，研究方向为卫星通信、大规模MIMO和波束赋形

邢智童：男，讲师，研究方向为无线通信传输、大规模MIMO和智能反射面，邮箱　xingzhitong@cqupt.edu.cn

李云：男，教授，研究方向为5G/6G移动通信技术、卫星通信和边缘计算

通讯作者:
邢智童　xingzhitong@cqupt.edu.cn

中图分类号: TN927.2
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- 文章访问数: 27
- HTML全文浏览量: 12
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- 被引次数: 0
出版历程
- 收稿日期: 2026-04-16
- 修回日期: 2026-07-09
- 录用日期: 2026-07-09
- 网络出版日期: 2026-07-25

Research on Adaptive Hybrid Beamforming Method for Massive MIMO LEO Satellite Communication Systems

XIAN Yongju^1
,,
HUANG Xiaolong¹,
XING Zhitong^{1
, ,},
LI Yun^{1, 2}

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62301100), The Scientific and Technological Research Program of Chongqing Municipal Education Commission Youth Project (KJQN202200606, KJQN202300638), Chongqing Natural Science Foundation (CSTB2024NSCQ-MSX0210, CSTB2024NSCQ-QCXMX0063, CSTC2024YCJH-BGZXM003), Chongqing Natural Science Foundation Innovation and Development Joint Fund (CSTB2025NSCQ-LZX0053)

摘要

摘要: 面向低地球轨道(LEO)卫星多用户下行大规模多输入多输出(MIMO)通信系统，该文研究结合低分辨率移相器(PSs)与天线-射频(RF)链自适应映射的混合波束赋形设计问题，以提升系统频谱效率。具体地，该文首先建立了LEO卫星多用户下行混合波束赋形系统模型，并以系统频谱效率最大化为目标构建优化问题。针对所构建的变量耦合非凸优化问题，该文通过加权最小均方误差(WMMSE)等价转化，并引入辅助变量对问题进行重构。在此基础上，结合块坐标下降(BCD)方法与惩罚对偶分解(PDD)方法进行交替迭代求解。进一步地，针对天线-RF链自适应映射设计，该文提出一种基于匈牙利算法的自适应映射方法，以获得在所构造线性指派模型下的最优映射结果。为降低大规模天线阵列下的计算开销，进一步提出一种基于天线优先级排序的自适应映射方法。仿真结果表明，该文所提方法相较于传统固定子阵和贪心动态子阵方案具有显著性能优势，并能够在系统性能与计算复杂度之间实现较好的权衡。
- LEO卫星 /
- 大规模MIMO /
- 低分辨率PSs /
- 自适应映射 /
- 混合波束赋形
Abstract: Objective With the growing demand for high-capacity and high-spectral-efficiency transmission in LEO satellite communications, massive MIMO has become a promising enabling technology. However, conventional fully digital beamforming is difficult to implement in practice due to the strict constraints on power consumption, hardware complexity, and payload cost of satellite platforms. Although partially connected hybrid beamforming can reduce hardware complexity, the conventional fixed subarray structure lacks flexibility and suffers from performance degradation, especially under low-resolution PSs constraints. To address this issue, this paper investigates adaptive antenna-RF chain mapping for hybrid beamforming design in LEO satellite massive MIMO systems. Methods This paper first establishes a system model for LEO satellite multi-user downlink massive MIMO hybrid beamforming and formulates a joint optimization problem with the objective of maximizing spectral efficiency. Considering the constant-modulus discrete phase constraints of low-resolution PSs, antenna-RF chain connection constraints, and transmit power constraint, the resulting problem is highly non-convex. To solve it, the original problem is transformed into an equivalent WMMSE formulation, and auxiliary variables are introduced to decouple the coupled variables. Based on this reformulation, a double-layer iterative optimization framework is developed by combining the BCD method and the PDD method. For adaptive antenna-RF chain mapping, the mapping problem is reformulated as a capacity-constrained linear assignment problem, and an optimal adaptive mapping method based on the Hungarian algorithm is proposed. Furthermore, to reduce the computational burden in large-scale antenna array scenarios, a low-complexity adaptive mapping method based on antenna priority sorting is developed. Results and Discussions Simulation results show that the proposed methods exhibit good convergence behavior. Specifically, the spectral efficiency increases rapidly in the initial iterations and then gradually converges, while the constraint violation decreases continuously, confirming the effectiveness of the proposed iterative optimization framework (Fig. 3). In terms of spectral efficiency, the proposed adaptive mapping methods consistently outperform the conventional fixed subarray and the existing greedy dynamic subarray scheme over different transmit powers and antenna scales. Among them, the Hungarian-based method achieving better spectral efficiency, whereas the antenna priority sorting-based method attains near-optimal performance with significantly reduced computational complexity (Figs. 4 and 5). As the number of PSs quantization bits increases, the system performance gradually approaches that of the continuous-PSs case, demonstrating the effectiveness of the proposed low-resolution PSs-based design (Fig. 6). In terms of energy efficiency, the proposed methods also outperform the conventional fully digital beamforming, fully connected, fixed subarray, and greedy dynamic subarray hybrid beamforming under different transmit powers and antenna scales (Figs. 7 and 8). Conclusions This paper proposes an adaptive hybrid beamforming design for LEO satellite massive MIMO systems under low-resolution PS constraints. By combining WMMSE reformulation with a PDD-BCD based optimization framework, joint design of digital precoding, analog precoding, and adaptive antenna-RF chain mapping is achieved. Simulation results demonstrate that the proposed methods provide superior performance in both spectral efficiency and energy efficiency. In particular, the Hungarian-based method provides better system performance, while the antenna priority sorting-based method achieves a favorable trade-off between performance and computational complexity. The proposed design provides an effective solution for high-performance hybrid beamforming in hardware-constrained LEO satellite massive MIMO systems.
- LEO Satellite /
- Massive MIMO /
- Low-resolution PSs /
- Adaptive Mapping /
- Hybrid Beamforming

HTML全文

图 1 低轨卫星大规模MIMO多用户下行通信系统

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图 2 自适应混合波束赋形系统架构

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图 3 频谱效率与卫星发射功率关系

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图 4 本文所提方案的收敛性

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图 5 频谱效率与卫星天线数的关系

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图 6 频谱效率与量化比特数的关系

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图 7 能量效率与卫星发射功率的关系

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图 8 能量效率与卫星天线数的关系

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