CMANet：基于合作多基站3D定位的通道掩码注意力网络
CMANet: Channel-Masked Attention Network for Cooperative Multi-Base-Station 3D Positioning
AI总结：
CMANet通过通道掩码注意力机制和轻量级LSTM解码器实现多基站协作的高精度3D定位，实验显示其在城市环境中具有优于现有方法的定位精度。
摘要：
实现无处不在的高精度定位对于下一代无线系统至关重要，但在多径丰富的城市环境中仍具有挑战性。通过利用嵌入在信道状态信息（CSI）中的细粒度多径特性，可以实现更可靠和精确的定位。为此，我们提出了CMANet，一种多基站协作定位架构，通过所提出的通道掩码注意力（CMA）机制对原始CSI进行特征级融合。CMA编码器将物理基础的先验知识——每个基站的信道增益——注入注意力权重中，从而强调可靠的链接并抑制虚假的多径。一个轻量级的LSTM解码器然后将子载波视为序列，将频域证据累积到最终的3D位置估计中。在典型的5G NR兼容的城市仿真中，CMANet实现了小于0.5米的中位误差和1.0米的90百分位误差，优于最先进的基准。消融实验验证了CMA和频率累积的必要性。CMANet可以边缘部署，并展示了与集成感知和通信（ISAC）对齐的多基站CSI定位协作范式。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
