Performance Analysis of Long Term Evaluation assisted global positioning system
Project heading:
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Long Term Evaluation assisted global positioning system
Tools:
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Software — > Matlab 2023a or above
OS –> Windows 10 [64 Bits]
Project workflow:
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Ø We construct the LTE network with n cells (base stations).
Ø The proposed work includes Four processes which are listed as follows,
(+) Measuring RSRP Value
(+) Collect Training Data
(+) Model Training
(+) Placing Random UE
(+) Evaluating the Minimum BS
Ø Measuring RSRP Value: We Measure the RSRP value of Each base station by placing a UE at a fixed position. (UMa Path Loss Model)
Ø Collect Training Data: We collect the training data for Randomly placed UE in the fixed cells based on the RSRP value.
Ø Model Training: We Train a Random Forest Model using the collected data.
Ø Placing Random UE: We Place a Random UE within the same cell, based on the RSRP value of all the cells.
Ø Evaluating the Minimum BS: We Evaluate the Minimum Base station required to approximate the UE with tolerable error.
Ø Finally, The performance of this research is evaluated by the following metrics,
(+) Number of Cells vs. RSRP value
(+) Number of UE Positions vs. RMSE
(+) Number of Base Stations vs. Error In UE Positions (cm)
