Neural Beam Field for Spatial Beam RSRP Prediction

• URL: http://arxiv.org/abs/2508.06956v2
• Date: Fri, 10 Oct 2025 15:23:25 GMT
• Title: Neural Beam Field for Spatial Beam RSRP Prediction
• Authors: Keqiang Guo, Yuheng Zhong, Xin Tong, Jiangbin Lyu, Rui Zhang,
• Abstract summary: Accurately predicting beam-level reference signal received power (RSRP) is essential for beam management in dense wireless networks.<n>This paper proposes Neural Beam Field (NBF), a hybrid neural-physical framework for efficient and interpretable spatial beam RSRP prediction.
• Score: 11.903931127386349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately predicting beam-level reference signal received power (RSRP) is essential for beam management in dense multi-user wireless networks, yet challenging due to high measurement overhead and fast channel variations. This paper proposes Neural Beam Field (NBF), a hybrid neural-physical framework for efficient and interpretable spatial beam RSRP prediction. Central to our approach is the introduction of the Multi-path Conditional Power Profile (MCPP), a learnable physical intermediary representing the site-specific propagation environment. This approach decouples the environment from specific antenna/beam configurations, which helps the model learn site-specific multipath features and enhances its generalization capability. We adopt a decoupled ``blackbox-whitebox" design: a Transformer-based deep neural network (DNN) learns the MCPP from sparse user measurements and positions, while a physics-inspired module analytically infers beam RSRP statistics. To improve convergence and adaptivity, we further introduce a Pretrain-and-Calibrate (PaC) strategy that leverages ray-tracing priors for physics-grounded pretraining and then RSRP data for on-site calibration. Extensive simulation results demonstrate that NBF significantly outperforms conventional table-based channel knowledge maps (CKMs) and pure blackbox DNNs in prediction accuracy, training efficiency, and generalization, while maintaining a compact model size. The proposed framework offers a scalable and physically grounded solution for intelligent beam management in next-generation dense wireless networks.

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