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

XIAN Yongju; HUANG Xiaolong; XING Zhitong; LI Yun

doi:10.11999/JEIT260458

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XIAN Yongju, HUANG Xiaolong, XING Zhitong, LI Yun. Research on Adaptive Hybrid Beamforming Method for Massive MIMO LEO Satellite Communication Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260458

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XIAN Yongju, HUANG Xiaolong, XING Zhitong, LI Yun. Research on Adaptive Hybrid Beamforming Method for Massive MIMO LEO Satellite Communication Systems[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260458

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

doi: 10.11999/JEIT260458 cstr: 32379.14.JEIT260458

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)

Received Date: 2026-04-16
Accepted Date: 2026-07-09
Rev Recd Date: 2026-07-09

Available Online: 2026-07-25

Abstract

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

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References(25)

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