The increasing deployment of large antenna arrays at base stations has significantly improved the spatial resolution and localization accuracy of radio-localization methods. However, traditional signal processing techniques struggle in complex radio environments, particularly in scenarios dominated by non line of sight (NLoS) propagation paths, resulting in degraded localization accuracy. Recent developments in machine learning have facilitated the development of machine learning-assisted localization techniques, enhancing localization accuracy in complex radio environments. However, these methods often involve substantial computational complexity during both the training and inference phases. This work extends the well-established fingerprinting-based localization framework by simultaneously reducing its memory requirements and improving its accuracy. Specifically, a model-based neural network is used to learn the location-to-channel mapping, and then serves as a generative neural channel model. This generative model augments the fingerprinting comparison dictionary while reducing the memory requirements. The proposed method outperforms fingerprinting baselines by achieving sub-wavelength localization accuracy, even in complex static NLoS environments. Remarkably, it offers an improvement by several orders of magnitude in localization accuracy, while simultaneously reducing memory requirements by an order of magnitude compared to classical fingerprinting methods.

翻译：基站大规模天线阵列的日益普及显著提升了无线电定位方法的空间分辨率与定位精度。然而，传统信号处理技术在复杂无线电环境中面临挑战，尤其是在非视距传播路径主导的场景下，定位精度往往严重下降。机器学习领域的最新进展推动了机器学习辅助定位技术的发展，有效提升了复杂环境中的定位精度。但这类方法通常在训练和推理阶段均涉及较高的计算复杂度。本研究通过同时降低内存需求与提升精度，对成熟的指纹定位框架进行了扩展。具体而言，我们采用基于模型的神经网络学习位置到信道的映射关系，并将其作为生成式神经信道模型。该生成模型在扩充指纹比对字典的同时降低了内存需求。所提方法在复杂静态非视距环境下仍能实现亚波长级定位精度，性能优于传统指纹定位基准。值得注意的是，与经典指纹定位方法相比，该方法在将定位精度提升数个数量级的同时，还将内存需求降低了一个数量级。