Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
arxiv(2024)
摘要
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.
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