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 6G LEO communication, multicast precoding is utilized to mitigate the significant inter-beam interference introduced by Full Frequency Reuse (FFR). However, traditional precoding algorithms are hindered by cubic computational complexity, making them unsuitable for massive MIMO. Additionally, existing user grouping strategies often fail to meet the fixed group size requirements of the DVB-S2X standard. Therefore, a joint optimization scheme is developed to integrate a low-complexity unsupervised deep learning model for precoding with improved user grouping algorithms to enhance sum rate and fairness. Methods To address the precoding challenge, an unsupervised deep learning model based on a hybrid CNN-LSTM architecture is proposed (Fig. 2). The Convolutional Neural Network (CNN) is utilized to extract spatial features from the Channel State Information (CSI), while the Long Short-Term Memory (LSTM) network captures their deep feature correlations. Unlike supervised learning, this model is trained by directly maximizing the sum rate as the loss function, subject to the Per-Antenna Constraint (PAC). For user grouping, two algorithms are developed to comply with the DVB-S2X standard. First, the CK-means algorithm is introduced, which modifies the standard K-means to ensure an equal number of users in each group. Second, FA-MAUG algorithm is proposed which prioritizes users with poor channel quality, thereby enhancing the overall robustness and fairness of the grouping result. Results and Discussions The intra-group similarity metric is employed to evaluate user grouping performance, which indicate that the CK-means algorithm achieves a similarity score approximately 0.1 higher than the MAUG algorithm and nearly 0.5 higher than random grouping across various group sizes (Fig. 3). This high similarity translates directly into better beamforming gain. In terms of throughput, the sum rate of the CNN-LSTM precoding combined with CK-means grouping outperforms traditional MMSE across different SNRs and total powers (Fig. 4, Fig. 5). The sum rate based on the proposed CNN-LSTM precoding scheme is on average 48.59% higher than that of the traditional MMSE algorithm and the gain of the CK-means algorithm compared to random grouping reaches 30.12% under different SNR conditions. Additionally, we analyzed the effects of the number of users per group, the number of groups, and the number of antennas on the system rate(Fig. 6, Fig. 7, Fig. 8), verifying the performance of the model in systems of different scales. Additionally, the complexity analysis confirms that the proposed precoding approach reduces the online overhead from the cubic level of traditional MMSE to a linear level relative to the number of antennas, which is critical for real-time deployment in massive MIMO satellite systems. Conclusions This paper presents a joint optimization framework for LEO satellite multicast systems, addressing the dual challenges of high computational complexity in precoding and lack of fairness in user grouping. Simulation results demonstrate that the proposed solution significantly enhances the system sum rate—by over 48.59% in typical SNR scenarios—compared to traditional methods. The approach provides a robust and scalable solution for the coordinated management of multi-beam interference in future 6G satellite communications. Future work will further explore the impact of hardware impairments and highly dynamic channel conditions on the generalization capabilities of the proposed model.
- LEO satellite communication,
- Multicast precoding,
- Unsupervised deep learning,
- User grouping,
- Per-antenna constraint

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References(27)

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