LEO Satellite Multi-beam Multicast Precoding and User Grouping Joint Optimization Algorithm

GUO Lili; FENG Yimeng; YUAN Peihong; GAO Yue

doi:10.11999/JEIT260375

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GUO Lili, FENG Yimeng, YUAN Peihong, GAO Yue. LEO Satellite Multi-beam Multicast Precoding and User Grouping Joint Optimization Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260375

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GUO Lili, FENG Yimeng, YUAN Peihong, GAO Yue. LEO Satellite Multi-beam Multicast Precoding and User Grouping Joint Optimization Algorithm[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260375

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LEO Satellite Multi-beam Multicast Precoding and User Grouping Joint Optimization Algorithm

doi: 10.11999/JEIT260375 cstr: 32379.14.JEIT260375

Institute of Space Internet, Fudan University, Shanghai 200438, China

Funds: The National Natural Science Foundation of China (62595745)

Received Date: 2026-03-31
Accepted Date: 2026-07-14
Rev Recd Date: 2026-07-14

Available Online: 2026-07-25

Abstract

Abstract

Objective In Sixth-Generation (6G) Low Earth Orbit (LEO) satellite communication systems, multicast precoding is adopted to mitigate severe inter-beam interference caused by Full Frequency Reuse (FFR). However, conventional precoding algorithms exhibit cubic computational complexity, limiting their applicability to massive Multiple-Input Multiple-Output (MIMO) systems. Existing user grouping methods also fail to satisfy the fixed group-size requirement specified by the DVB-S2X standard. To address these limitations, a joint optimization framework is proposed that combines a low-complexity unsupervised deep learning-based precoding model with improved user grouping algorithms to improve the system sum rate and fairness. Methods An unsupervised deep learning model based on a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture is proposed for precoding (Fig. 2). The Convolutional Neural Network (CNN) extracts spatial features from Channel State Information (CSI), while the Long Short-Term Memory (LSTM) network captures high-level feature correlations. The model is trained by directly maximizing the system sum rate while satisfying the Per-Antenna power Constraint (PAC), without requiring supervised labels. For user grouping, two algorithms compatible with the DVB-S2X standard are developed. First, the CK-means algorithm extends conventional K-means clustering to ensure an equal number of users in each group while preserving high intra-group channel similarity. Second, the Fairness-Aware MAUG (FA-MAUG) algorithm prioritizes users with poor channel conditions during grouping, thereby improving system robustness and fairness. Results and Discussions The intra-group similarity metric is used to evaluate user grouping performance. The results show that the CK-means algorithm achieves an average similarity approximately 0.1 higher than that of the MAUG algorithm and nearly 0.5 higher than that of random grouping across different group sizes (Fig. 3), resulting in improved beamforming gain. In terms of system sum rate, the proposed CNN-LSTM precoding combined with CK-means grouping consistently outperforms the conventional Minimum Mean Square Error (MMSE) algorithm under different Signal-to-Noise Ratios (SNRs) and total transmit power levels (Fig. 4 and Fig. 5). Under different SNR conditions, the proposed CNN-LSTM precoding scheme improves the average system sum rate by 48.59% compared with the MMSE algorithm, whereas CK-means grouping increases the average system sum rate by 30.12% relative to random grouping. The effects of the number of users per group, the number of groups, and the number of antennas are further evaluated (Fig. 6-Fig. 8), demonstrating that the proposed framework maintains superior performance across systems of different scales. Complexity analysis further shows that the proposed precoding method reduces the online computational complexity from the cubic complexity of the conventional MMSE algorithm to linear complexity with respect to the number of antennas, making it well suited for real-time deployment in large-scale LEO satellite communication systems. Conclusions A joint optimization framework is proposed for LEO satellite multicast communication systems to address the high computational complexity of precoding and the limited fairness of conventional user grouping methods. Simulation results demonstrate that the proposed framework substantially improves the system sum rate, achieving an average gain of 48.59% over the conventional MMSE algorithm under different SNR conditions while simultaneously reducing online computational complexity. The proposed framework provides an effective and scalable solution for multi-beam interference mitigation and resource optimization in future 6G LEO satellite communication systems.
- LEO satellite communication,
- Multicast precoding,
- Unsupervised deep learning,
- User grouping,
- Per-Antenna power Constraint(PAC)

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References(27)

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