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.

我们设计了一种利用最小描述特征来大大降低深度学习网络复杂性的新型定位神经网络（P-NN）。通过最大功率测量及其时间位置传达进行定位所需的信息，使 P-NN 的学习能力得到改进，同时在稀疏图像和测量矩阵上实现自注意力机制以强化网络的训练能力，并通过信息论量化信号 bin 选择上的期望信息增益和分类能力进行特征空间大小的优化。数值结果表明，P-NN 在性能复杂度平衡方面相比利用完整的功率延迟特征（PDP）的深度学习基准有显著的优势，尤其在低信噪比情况下，通过我们的最小描述特征，不必要的测量结果被舍弃，取得了很大的性能改进。

基于机器学习的无线定位中最小描述特征选择的复杂度降低

Minimum Description Feature Selection for Complexity Reduction in  Machine Learning-based Wireless Positioning

A recent line of research has been investigating deep learning approaches to
wireless positioning (WP). Although these WP algorithms have demonstrated high
accuracy and robust performance against diverse channel conditions, they also
have a major drawback: they require processing high-dimensional features, which
can be prohibitive for mobile applications. In this work, we design a
positioning neural network (P-NN) that substantially reduces the complexity of
deep learning-based WP through carefully crafted minimum description features.
Our feature selection is based on maximum power measurements and their temporal
locations to convey information needed to conduct WP. We also develop a novel
methodology for adaptively selecting the size of feature space, which optimizes
over balancing the expected amount of useful information and classification
capability, quantified using information-theoretic measures on the 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).

本研究通过设计一种位置神经网络（P-NN），结合最大功率测量和时间位置，减少了深度学习的复杂度，实现了性能和复杂度之间的优势平衡。

基于机器学习的无线定位中的复杂度简化：最小描述特征

Complexity Reduction in Machine Learning-Based Wireless Positioning:  Minimum Description Features

We investigate the application of the factor graph framework for blind joint
channel estimation and symbol detection on time-variant linear inter-symbol
interference channels. In particular, we consider the expectation maximization
(EM) algorithm for maximum likelihood estimation, which typically suffers from
high complexity as it requires the computation of the symbol-wise posterior
distributions in every iteration. We address this issue by efficiently
approximating the posteriors using the belief propagation (BP) algorithm on a
suitable factor graph. By interweaving the iterations of BP and EM, the
detection complexity can be further reduced to a single BP iteration per EM
step. In addition, we propose a data-driven version of our algorithm that
introduces momentum in the BP updates and learns a suitable EM parameter update
schedule, thereby significantly improving the performance-complexity tradeoff
with a few offline training samples. Our numerical experiments demonstrate the
excellent performance of the proposed blind detector and show that it even
outperforms coherent BP detection in high signal-to-noise scenarios.

我们研究了因子图框架在时间变化的线性符号间干扰信道上盲联合信道估计和符号检测的应用。我们提出了一种基于置信传播算法的后验概率的有效逼近方法，并通过交织后验传播和期望最大化算法的迭代，将检测复杂性进一步降低为每个期望最大化步骤的单个后验传播迭代。此外，我们还提出了一种数据驱动的算法版本，通过引入动量来更新后验传播，并学习合适的期望最大化参数更新调度，从而在少量离线训练样本的情况下显著提高性能与复杂性之间的权衡。数值实验证明了所提出的盲检测器的出色性能，甚至在高信噪比情况下优于相干后验传播检测。