Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
This work addresses the computational burden of wireless positioning for mobile applications, representing an incremental improvement in feature selection.
The paper tackles the high complexity of deep learning-based wireless positioning by designing a neural network that uses minimum description features, achieving a significant advantage in performance-complexity tradeoff over baselines using full power delay profiles.
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).