Modeling and Characterization of Broadband Earth-Moon-Earth Communication Channels

LI Chengqian; QIAN Xiaowei; HU Xiaoling

doi:10.11999/JEIT251028

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LI Chengqian, QIAN Xiaowei, HU Xiaoling. Modeling and Characterization of Broadband Earth-Moon-Earth Communication Channels[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251028

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LI Chengqian, QIAN Xiaowei, HU Xiaoling. Modeling and Characterization of Broadband Earth-Moon-Earth Communication Channels[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251028

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Modeling and Characterization of Broadband Earth-Moon-Earth Communication Channels

doi: 10.11999/JEIT251028 cstr: 32379.14.JEIT251028

School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Accepted Date: 2026-03-24
Rev Recd Date: 2026-03-24

Available Online: 2026-04-21

Abstract

Abstract

Objective This paper presents a comprehensive channel model for wideband Earth-Moon-Earth (EME) communication, tackling the shortcomings of traditional simplified models that cannot accurately represent the Moon’s complex scattering behavior and terrain-induced effects. Existing approaches, which treat the Moon as a point reflector or depend on empirical scattering laws, are inadequate for broadband, high-capacity systems. To address this, a unified large-scale link model is proposed to statistically capture terrain-driven reflection characteristics, while a small-scale model systematically analyzes multipath and Doppler effects, decomposing the channel and quantifying dynamic impairments. Link-level simulations validate the model’s accuracy. This work fills a critical gap in broadband EME channel modeling, providing a necessary foundation for the design and optimization of future deep space communication systems. Methods A dual-scale modeling approach is proposed for wideband Earth-Moon-Earth (EME) channels. At the large scale, a unified integral path loss model is developed for both wide- and narrow-beam scenarios, with lunar terrain statistically represented by a Gaussian height distribution to capture shadowing and roughness effects. A distributed integration method is used to compute effective RCS under narrow-beam conditions. At the small scale, the channel is decomposed into quasi-specular and diffuse components, with delay-power profiles derived from surface roughness and scattering mechanisms. Doppler shift and spread are analytically modeled based on Earth-Moon orbital dynamics. Monte Carlo simulations and numerical integration verify the models, and system-level performance is evaluated in terms of BER under various channel conditions with different equalization and frequency offset correction schemes. Results and Discussions A comprehensive channel model is developed to capture both large- and small-scale fading in wideband Earth-Moon-Earth (EME) communication. The large-scale model, validated by simulations, accurately represents the non-uniform power distribution across the lunar disk through an integrated RCS approach. At the small scale, quasi-specular and diffuse components characterize multipath delay spread, while the Doppler model quantifies effects from Earth’s rotation and lunar orbital motion, with a two-way shift of ～4.5 kHz and a spread of ±39.88 Hz at 1.296 GHz. Low-SNR simulations show that conventional equalizers (LMS, RLS, RAKE) stagnate near BER = 0.1, and frequency correction methods (FFT-based, MLE) degrade under large frequency offsets, highlighting the challenges of accurate compensation. Conclusions This paper develops and validates a comprehensive channel model for broadband Earth-Moon-Earth (EME) communication. The model more accurately predicts path loss, shadowing, multipath delay, and Doppler effects than conventional point-target or empirical methods. Results show that lunar terrain and surface properties cause severe signal degradation, which traditional equalization and frequency correction cannot effectively mitigate. Future work should integrate high-resolution lunar DEMs and measured RCS data to improve accuracy and explore adaptive methods, such as machine learning, to handle severe delay spread. This model offers a foundation for reliable EME links and future deep-space communication networks.
- EME,
- Path loss,
- Radar cross section,
- Multipath channel,
- Doppler effect

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References(21)

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