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arXiv · 2602.00696

CMANet: Channel-Masked Attention Network for Cooperative Multi-Base-Station 3D Positioning

Tong An·Huan Lu·Jiayang Shi·Kai Yu·Rongrong Zhu·Bin Zheng·Jiwei Zhao·Haibo Zhou
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Abstract

Achieving ubiquitous high-accuracy localization is crucial for next-generation wireless systems, yet remains challenging in multipath-rich urban environments. By exploiting the fine-grained multipath characteristics embedded in channel state information (CSI), more reliable and precise localization can be achieved. To address this, we present CMANet, a multi-BS cooperative positioning architecture that performs feature-level fusion of raw CSI using the proposed Channel Masked Attention (CMA) mechanism. The CMA encoder injects a physically grounded prior--per-BS channel gain--into the attention weights, thus emphasizing reliable links and suppressing spurious multipath. A lightweight LSTM decoder then treats subcarriers as a sequence to accumulate frequency-domain evidence into a final 3D position estimate. In a typical 5G NR-compliant urban simulation, CMANet achieves less than 0.5m median error and 1.0m 90th-percentile error, outperforming state-of-the-art benchmarks. Ablations verify the necessity of CMA and frequency accumulation. CMANet is edge-deployable and exemplifies an Integrated Sensing and Communication (ISAC)-aligned, cooperative paradigm for multi-BS CSI positioning.

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Tong An, Huan Lu, Jiayang Shi, Kai Yu, Rongrong Zhu, Bin Zheng, Jiwei Zhao, Haibo Zhou. 2026-01-31. CMANet: Channel-Masked Attention Network for Cooperative Multi-Base-Station 3D Positioning. https://arxiv.org/abs/2602.00696

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