Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN’s feature selection strategy is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We improve P-NN’s learning ability by intelligently processing two different types of inputs: sparse image and measurement matrices. Specifically, we implement a self-attention layer to reinforce the training ability of our network. We also develop a technique to adapt feature space size, optimizing over the expected information gain and the classification capability quantified with information-theoretic measures on signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP). In particular, we find that P-NN achieves a large improvement in performance for low SNR, as unnecessary measurements are discarded in our minimum description features.

中文翻译：

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用于降低基于机器学习的无线定位复杂性的最小描述特征选择


最近，深度学习方法为无线定位（WP）中的难题提供了解决方案。尽管这些 WP 算法在复杂的信道环境中获得了出色且一致的性能，但处理高维特征所带来的计算复杂性对于移动应用程序来说可能令人望而却步。在这项工作中，我们设计了一种新颖的定位神经网络（P-NN），它利用最少的描述特征来大幅降低基于深度学习的 WP 的复杂性。 P-NN 的特征选择策略基于最大功率测量及其时间位置，以传达进行 WP 所需的信息。我们通过智能处理两种不同类型的输入：稀疏图像和测量矩阵来提高 P-NN 的学习能力。具体来说，我们实现了一个自注意力层来增强我们网络的训练能力。我们还开发了一种适应特征空间大小的技术，优化预期信息增益和通过信号箱选择的信息论测量量化的分类能力。数值结果表明，与利用全功率延迟分布 (PDP) 的深度学习基线相比，P-NN 在性能与复杂性权衡方面取得了显着优势。特别是，我们发现 P-NN 在低 SNR 的性能方面取得了很大的进步，因为我们的最小描述特征中丢弃了不必要的测量。