2 research outputs found
Genetic Algorithm Optimized Grid-based RF Fingerprint Positioning in Heterogeneous Small Cell Networks
In this paper we propose a novel optimization
algorithm for grid-based RF fingerprinting to improve
user equipment (UE) positioning accuracy. For this
purpose we have used Multi-objective Genetic Algorithm
(MOGA) which enables autonomous calibration of gridcell
layout (GCL) for better UE positioning as compared to
that of the conventional fingerprinting approach.
Performance evaluations were carried out using two
different training data-sets consisting of Minimization of
Drive Testing measurements obtained from a dynamic
system simulation in a heterogeneous LTE small cell
environment. The robustness of the proposed method has
been tested analyzing positioning results from two
different areas of interest. Optimization of GCL is
performed in two ways: (1) array-wise calibration of the
grid-cell units using non-overlapping GCL and (2) creating
an overlapping GCL to cover of whole simulation area
with different rectangular grid-cell units. Simulation
results show that if sufficient amount of training data is
available then the proposed method can improve
positioning accuracy of 56.74% over the conventional gridbased
RF fingerprinting.peerReviewe