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<p>Deep learning based wireless channel prediction can benefit from full exploitation of propagation features, yet how to construct effective inputs for accurate channel prediction remains under-explored. This paper presents a deep learning-based approach that optimizes environmental input construction for accurate channel path loss prediction. A campus measurement campaign is conducted to obtain datasets, where satellite image and semantic layers are aligned at a unified environmental granularity. Using a fixed network backbone and identical training protocol, results show that channel prediction accuracy is sensitive to spatial coverage. Overly local patches are insufficient, while enlarging coverage to include propagation-relevant surroundings yields error reduction with diminishing returns. It is further found that orientation normalization, implemented by aligning the Tx-Rx direction to a fixed reference axis, improves performance by reducing geometric variability. Building on this spatial extent, Shapley-based attribution and ablation analysis indicate that buildings and roads have dominating impacts, vegetation offers complementary information, and location-related descriptors mainly contribute through interactions. The results validate that the input feature construction improves channel prediction accuracy and provides guidance for future intelligent channel prediction.</p>

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Deep learning-based wireless channel prediction with propagation feature exploitation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Zhicheng Qiu,
  • Ruisi He,
  • Bo Ai,
  • Mi Yang,
  • Yuan Yuan,
  • Chenlong Wang,
  • Yuxin Zhang,
  • Tianyu Shao,
  • Zhangdui Zhong

摘要

Deep learning based wireless channel prediction can benefit from full exploitation of propagation features, yet how to construct effective inputs for accurate channel prediction remains under-explored. This paper presents a deep learning-based approach that optimizes environmental input construction for accurate channel path loss prediction. A campus measurement campaign is conducted to obtain datasets, where satellite image and semantic layers are aligned at a unified environmental granularity. Using a fixed network backbone and identical training protocol, results show that channel prediction accuracy is sensitive to spatial coverage. Overly local patches are insufficient, while enlarging coverage to include propagation-relevant surroundings yields error reduction with diminishing returns. It is further found that orientation normalization, implemented by aligning the Tx-Rx direction to a fixed reference axis, improves performance by reducing geometric variability. Building on this spatial extent, Shapley-based attribution and ablation analysis indicate that buildings and roads have dominating impacts, vegetation offers complementary information, and location-related descriptors mainly contribute through interactions. The results validate that the input feature construction improves channel prediction accuracy and provides guidance for future intelligent channel prediction.