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AI-Driven Fronthaul Link Compression in Wireless Communication Systems: Review and Method Design

Created by
  • Haebom

Author

Keqin Zhang

Outline

This paper investigates AI-based compression techniques for transmitting high-dimensional signals under strict bandwidth and latency constraints in the fronthaul link of wireless systems. Conventional strategies such as compressed sensing, scalar quantization, and fixed codec pipelines rely on limited prior information, suffer from rapid performance degradation at high compression ratios, and are difficult to tune across channels and deployment environments. In this paper, we investigate AI-based compression techniques and analyze two representative high-compression approaches: CSI feedback via end-to-end learning and resource block (RB)-level precoding optimization and compression combining. Based on these insights, we propose a fronthaul compression strategy tailored to cell-free architectures, aiming for high compression ratios, controllable performance loss, RB-level rate adaptation, and low-latency inference suitable for centralized, cooperative transmission in next-generation networks.

Takeaways, Limitations

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Takeaways:
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We propose the possibility of efficient transmission of high-dimensional signals in the fronthaul link of wireless systems by utilizing AI-based compression technology.
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We propose a high-compression fronthaul compression strategy specialized for cell-free architectures.
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It demonstrates the potential to meet the requirements of next-generation networks by supporting RB-level rate adaptation and low-latency inference.
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We present a strategy to achieve high compression ratios through end-to-end learning and RB-level precoding optimization.
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Limitations:
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The scalability of the proposed cell-free architecture-specific strategy to general wireless systems needs to be examined.
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Further research is needed to determine its practical utility, as actual system implementation and performance evaluation results are not presented.
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There is a lack of analysis on the energy efficiency of the proposed method.
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