Data Driven Optimization of Inter-Frequency Mobility Parameters for Emerging Networks

Simple item page
dc.contributor.advisorImran, Ali
dc.contributor.authorUmar Bin Farooq, Muhammad
dc.contributor.committeeMemberRefai, Hazem
dc.contributor.committeeMemberCheng, Samuel
dc.date.accessioned2021-12-21T20:55:00Z
dc.date.available2021-12-21T20:55:00Z
dc.date.issued2021
dc.date.manuscript2021-12-03
dc.description.abstractDensification and multi-band operation means inter-frequency handovers can become a bottleneck for mobile user experience in emerging cellular networks. The challenge is aggravated by the fact that there does not exist a method to optimize key inter-frequency handover parameters namely A5 time-to-trigger, A5-threshold1 and A5-threshold2. This thesis presents a first study to analyze and optimize the three A5 parameters for jointly maximizing three key performance indicators that reflect mobile user experience: handover success rate (HOSR), reference signal received power (RSRP), and signal-to-interference-plus-noise-ratio (SINR). As analytical modeling cannot capture the system-level complexity, we exploit a data-driven approach. To minimize the training data generation time, we exploit shapley additive explanations (SHAP) sensitivity analysis. The insights from SHAP analysis allow the selective collection of the training data thereby enabling the easier implementation of the proposed solution in a real network. We show that joint RSRP, SINR and HOSR optimization problem is non-convex and solve it using genetic algorithm (GA). We then propose an intelligent mutation scheme for GA, which makes the solution 5x times faster than the legacy GA and 21x faster than the brute force search. This thesis thus presents first solution to implement computationally efficient closed-loop self-optimization of inter-frequency mobility parameters.en_US
dc.identifier.urihttps://hdl.handle.net/11244/332557
dc.languageen_USen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectHandover Optimizationen_US
dc.subjectMobility Managementen_US
dc.subjectHandover Managementen_US
dc.subject5Gen_US
dc.subject6Gen_US
dc.thesis.degreeMaster of Scienceen_US
dc.titleData Driven Optimization of Inter-Frequency Mobility Parameters for Emerging Networksen_US
ou.groupGallogly College of Engineering::School of Electrical and Computer Engineeringen_US
shareok.nativefileaccessrestricteden_US
shareok.orcid0000-0001-8034-6965en_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2021_Umar Bin Farooq_Muhammad_Thesis.pdf
Size:
10.5 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

OU - Theses