发表机构
University of Ottawa（渥太华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
LocAttMamba提出一种低复杂度室内定位框架，利用Mamba编码器处理各AP特征，通过多头注意力融合，在5G和UWB数据集上分别实现1.004米和0.599米的定位误差，且计算量最小、推理速度比Transformer快4.4-16倍。
AI 中文摘要
在第五代（5G）和第六代（6G）网络中，准确且低复杂度的室内定位对于基于位置的服务至关重要，因为定位设备在有限的计算预算和非理想条件下运行。室内定位已被广泛研究，传统方法多采用信号级定位技术。然而，这些技术通常在非视距（NLoS）场景下性能下降。近年来，包括Transformer在内的人工智能（AI）技术已被用于应对这些挑战。虽然基于Transformer的架构能够捕获从分布式接入点（AP）收集的测量值之间的依赖关系，但其高计算复杂度导致大量的乘加运算和较长的推理时间。相比之下，轻量级循环和卷积模型以牺牲精度为代价来降低计算成本。在本文中，我们提出LocAttMamba，一种低复杂度定位框架，其中每个AP的信道冲激响应（CIR）和基于时间的特征由独立的Mamba编码器处理，该编码器具有近线性复杂度，生成的每个AP嵌入通过多头注意力层融合，该层根据每个AP在每个时间步的重要性对其加权。该框架联合预测用户位置及其逐轴不确定性，并通过事后校准步骤进行细化。我们使用两个真实的5G和超宽带（UWB）测量数据集评估所提出的框架。数值结果表明，LocAttMamba在5G和UWB数据集上分别获得平均二维（2-D）定位误差1.004米和0.599米，优于第二好的基准方法9.79%和5.82%，同时在所有评估模型中所需的乘加运算最少，并且比基于Transformer的基准方法快4.4至16倍。
英文摘要
Accurate and low-complexity indoor localization is important for location-based services in fifth generation (5G) and sixth generation (6G) networks, where positioning devices operate under limited computational budgets and non conditions. Indoor localization has been studied widely using traditional signal-level localization approaches. However, these techniques often show degraded performance in on-line-of-sight (NLoS) scenarios. Recently, artificial intelligence (AI)-based techniques, including transformers, have been applied to address these challenges. While transformer-based architectures can capture the dependencies within the measurements collected from distributed access points (APs), their high computational complexity results in a large number of multiply-accumulate operations and long inference time. In contrast, lightweight recurrent and convolutional models trade this cost for degraded accuracy. In this paper, we propose LocAttMamba, a low-complexity localization framework in which the channel impulse response (CIR) and time-based features of each AP are processed by a separate Mamba encoder with near-linear complexity, and the resulting per-AP embeddings are fused through a multi-head attention layer that weights each AP according to its importance at every time step. The framework jointly predicts the user location and its per-axis uncertainty, which is refined through a post-hoc calibration step. We evaluate the proposed framework using two real-world 5G and ultra-wideband (UWB) measurement datasets. Our numerical results reveal that LocAttMamba obtains a mean two-dimensional (2-D) positioning error of 1.004 m and 0.599 m, respectively, on 5G and UWB datasets, outperforming the second-best benchmark by 9.79% and 5.82%, while requiring the fewest multiply-accumulate operations among all evaluated models and being 4.4-16 times faster than the transformer-based benchmarks.
