Hybrid Precoding and Beamforming Algorithms for Cell-Free Satellite Networks
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 21
Special Issue Editors
Interests: hybrid precoding and beamforming; machine learning; distributed optimization
Interests: high-mobility communications; satellite networks; delay-Doppler domain communications
Special Issue Information
Dear Colleagues,
Cell-free satellite networks, which provide global coverage through collaborative distributed satellite nodes, represent a core technology for 6G space–air–ground integrated communications. Hybrid precoding and beamforming, as key methods to enhance spectral efficiency and anti-interference capabilities, face challenges in dynamic channel modeling, multi-node collaborative optimization, and balancing complexity with energy consumption in millimeter-wave band satellite communications. Recent advancements in AI-driven adaptive algorithms (e.g., machine learning-based beamforming) have become an important means of overcoming technical barriers in this field.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following topic:
- AI algorithm design and optimization for cell-free satellite networks;
- Advanced AI algorithms for hybrid dynamic precoding algorithms;
- Deep reinforcement learning-based algorithms for hybrid precoding and beamforming decision;
- Robust algorithms under non-ideal channel conditions;
- AI-driven multi-objective cross-layer optimization algorithms;
- Advanced channel estimation algorithms for cell-free satellite networks;
- Advanced AI algorithms for delay-Doppler systems.
Dr. Peng Lin
Dr. Yao Xu
Prof. Dr. Zhizhong Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- deep reinforcement learning
- distributed optimization
- hybrid precoding and beamforming
- channel estimation
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