LLM Channel Prediction Method for TDD OTFS Low-Earth-Orbit Satellite Communication Systems

YOU Yuxin; JIANG Xinglong; LIU Huijie; LIANG Guang

doi:10.11999/JEIT250105

Volume 47 Issue 8

Aug. 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(8): 2535-2548

YOU Yuxin, JIANG Xinglong, LIU Huijie, LIANG Guang. LLM Channel Prediction Method for TDD OTFS Low-Earth-Orbit Satellite Communication Systems[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2535-2548. doi: 10.11999/JEIT250105

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YOU Yuxin, JIANG Xinglong, LIU Huijie, LIANG Guang. LLM Channel Prediction Method for TDD OTFS Low-Earth-Orbit Satellite Communication Systems[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2535-2548. doi: 10.11999/JEIT250105

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LLM Channel Prediction Method for TDD OTFS Low-Earth-Orbit Satellite Communication Systems

doi: 10.11999/JEIT250105 cstr: 32379.14.JEIT250105

YOU Yuxin^{1, 2, 3},
JIANG Xinglong^{1, 3},
LIU Huijie^{1, 3
,
,},
LIANG Guang^{1, 3}

1.
Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201203, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Shanghai Engineering Center for Microsatellites, Shanghai 201203, China

Funds: The National Natural Science Foundation of China (U21A20443)

Received Date: 2025-02-24
Rev Recd Date: 2025-04-02

Available Online: 2025-05-12

Publish Date: 2025-08-27

Abstract

Abstract

Orthogonal Time Frequency Space (OTFS) modulation shows promise in Low Earth Orbit (LEO) satellite-to-ground communications. However, rapid Doppler shift variation and high latency in LEO systems lead to channel aging. Real-time channel estimation increases the computational complexity of onboard receivers and reduces transmission efficiency due to substantial pilot overhead. This study addresses a Ka-band Multiple-Input Single-Output (MISO) OTFS satellite-to-ground communication system by designing a Downlink (DL) channel prediction scheme based on Uplink (IL) channel estimation. A high-precision channel estimation method is proposed, combining matched filtering with data detection to extract UL Channel State Information (CSI). An Adaptive Sparse Large Language Model (ASLLM)-based channel prediction network is then constructed to predict DL CSI. Compared with existing methods, simulations show that the proposed approach achieves lower Normalized Mean Square Error (NMSE) and Bit Error Rate (BER), with improved generalization across multiple scenarios and within an acceptable computational complexity range. Objective LEO satellite communication systems offer advantages over Medium-Earth-Orbit (MEO) and Geostationary-Earth-Orbit (GEO) systems, particularly in terms of reduced transmission latency and lower path loss. Therefore, LEO satellites are considered a key element of the Sixth-Generation (6G) Non-Terrestrial Network (NTN) satellite internet architecture. However, high-mobility channels between LEO satellites and ground stations introduce significant challenges for conventional Orthogonal Frequency Division Multiplexing (OFDM), resulting in marked performance degradation. OTFS modulation, which operates in the Delay-Doppler (DD) domain, has been shown to outperform OFDM in high-mobility scenarios, Multiple-Input Multiple-Output (MIMO) systems, and millimeter-wave frequency bands. This performance advantage is attributed to its robustness to Doppler shifts and inter-symbol interference. In modern Time Division Duplexing (TDD) satellite communication systems, OTFS receivers require high-complexity real-time channel estimation, and transmitters rely on extensive pilot overhead to encode CSI for reliable data recovery. To mitigate these limitations, channel prediction schemes using UL CSI to predict DL CSI have been proposed. However, broadband MISO-OTFS systems with large antenna arrays and high-resolution transmission demand precise and efficient CSI prediction under rapidly varying DD-domain conditions. The dynamic and rapidly aging characteristics of DD domain CSI present significant challenges for accurate prediction in broadband, high-mobility, and large-scale antenna communication systems. To address this, an ASLLM-based channel prediction method is developed. The proposed method enables accurate prediction of DD-domain CSI under these conditions. Methods By modeling the input–output relationship of a MISO OTFS satellite-to-ground communication system, this study proposes a data-assisted fractional Doppler matched filtering algorithm for channel estimation. This method leverages the shift property of correlation functions and integrates iterative optimization through Minimum Mean Square Error (MMSE) signal detection to achieve accurate estimation of DD domain CSI. The resulting high-precision CSI serves as a reliable input for the subsequent prediction network. The task of predicting DL slot CSI from UL slot CSI is formulated as a minimization of the NMSE between the network’s predicted CSI and the true DL CSI. The proposed ASLLM prediction network consists of a preprocessing layer, an embedding layer, a Generative Pre-trained Transformer (GPT) layer, and an output layer. The raw DD-domain CSI is first processed through the preprocessing layer to extract convolutional features. In the embedding layer, a value attention module and a position attention module are applied to convert the CSI features into a structured, text-like input suitable for GPT processing. The value attention module adaptively extracts sparse feature values of the CSI, while the position attention module encodes positional characteristics in a non-trainable manner. The core of the prediction network is a pre-trained, open-source GPT-2 backbone, which is used to model and forecast the CSI sequence. The network output is then passed through a linear transformation layer to recover the predicted DD-domain CSI. Results and Discussions The satellite-to-ground channel is modeled using the NTN-TDL-D dual mobility channel and simulated with QuaDRiGa. First, the performance of the data-assisted matched filtering channel estimation method is validated (Fig. 7). At a Signal-to-Noise Ratio (SNR) of 20 dB, the BER reaches the order of 0.001 after three iterations. Next, training loss curves for several neural network models are compared (Fig. 8). The ASLLM model exhibits the fastest convergence and highest stability. It also achieves superior NMSE and BER performance in MMSE data detection compared with other approaches (Fig. 9). ASLLM demonstrates strong generalization across different channel models and varying terminal velocities (Fig. 10). However, in cross-frequency generalization scenarios, a small number of additional training samples are still required to maintain accuracy (Fig. 11). Finally, ablation experiments confirm the contribution of each core module within the ASLLM architecture (Table 2). Comparisons of network parameters, training time, and inference time indicate that the computational complexity of ASLLM remains within an acceptable range (Table 3). Conclusions This study proposes a channel prediction method for TDD MISO OTFS systems, termed ASLLM, tailored to high-mobility scenarios such as communication between LEO satellites and high-speed trains. The approach leverages high-precision historical UL CSI, obtained through a data-assisted matched filtering algorithm, to predict future DL CSI. By extracting sparse features from DD domain CSI, the method fine-tunes a pre-trained GPT-2 model—originally trained on general knowledge—to improve predictive accuracy. Simulation results show that: (1) considering both computational complexity and estimation accuracy, optimal stopping criteria for the channel estimation algorithm are defined as an iteration number of 3 and a threshold of 0.001; (2) ASLLM outperforms existing prediction methods in terms of convergence speed, NMSE, BER, and generalization capability; and (3) each module of the network contributes effectively to performance, while overall computational complexity remains within a feasible range.
- Orthogonal Time Frequency Space (OTFS),
- Low earth orbit satellites,
- Channel prediction,
- Large Language Model (LLM)

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References(33)

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