Complexity Reduction in Machine Learning-Based Wireless Positioning:
  Minimum Description Features
Myeung Suk Oh Anindya Bijoy Das Taejoon Kim David J. Love Christopher G. Brinton
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).