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

Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton

Research output: Contribution to journalArticlepeer-review

Abstract

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).

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Journal on Selected Areas in Communications
DOIs
StateAccepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Keywords

  • Accuracy
  • Convolutional neural network
  • Feature extraction
  • Kullback-Leibler (KL) divergence
  • minimum description length (MDL)
  • Neural networks
  • self-attention
  • Sensors
  • Signal to noise ratio
  • Wireless communication
  • wireless positioning
  • Wireless sensor networks

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