A Quadrilateral Geometry Classification Method and Device for Femtocell Positioning Networks

Area of Science:

• Wireless communication systems
• Signal processing
• Machine learning applications in telecommunications

Background:

• Accurate positioning of macrocell user equipment (MUE) is crucial in cellular networks.
• Existing methods for determining optimal base station geometry can be computationally intensive.
• The geometric dilution of precision (GDOP) is a key metric for evaluating positioning accuracy.

Purpose of the Study:

• To propose a Normalization Multi-Layer Perception (NMLP) geometry classifier for autonomously determining optimal femtocell evolved Node B (FeNB) configurations.
• To enhance the accuracy of MUE positioning using Time Difference of Arrival (TDOA) measurements.
• To minimize the GDOP value for improved location estimation.

Main Methods:

• Development of an Iterative Geometry Training (IGT) algorithm to generate training data for the NMLP classifier.
• Implementation of the NMLP geometry classifier on a cloud computing platform.
• Utilizing a specific neural network architecture with six by six neurons in two hidden layers for faster convergence.
• Conducting numerical simulations to validate the proposed method.

Main Results:

• The proposed NMLP geometry classifier successfully identifies optimal FeNB dispositions for MUE positioning.
• Simulation results demonstrate the method's feasibility and effectiveness, particularly in scenarios with numerous FeNBs.
• The chosen neural network architecture significantly reduces convergence time.
• Validation through analysis of three quadrilateral optimum geometry disposition decision criteria.

Conclusions:

• The NMLP geometry classifier offers an efficient and autonomous solution for optimizing FeNB placement in MUE positioning systems.
• The method shows significant promise for large-scale deployments with a high density of FeNBs.
• The IGT algorithm and NMLP architecture provide a robust framework for achieving low GDOP values in TDOA-based positioning.