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Optimizing Vehicular Users Association in Urban Mobile Networks

Geymerson S. Ramos, Razvan Stanica, Rian G. S. Pinheiro, Andre L. L. Aquino

TL;DR

An efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity and reduces the execution time by more than 80% compared to an exact method, while achieving optimal solutions.

Abstract

This study aims to optimize vehicular user association to base stations in a mobile network. We propose an efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity. We evaluate this solution using real-world base station locations from São Paulo, Brazil, and the SUMO mobility simulator. We compare our approach against a state of the art solution which uses route prediction, maintaining or surpassing the provided quality of service with the same number of handover operations. Additionally, the proposed solution reduces the execution time by more than 80\% compared to an exact method, while achieving optimal solutions.

Optimizing Vehicular Users Association in Urban Mobile Networks

TL;DR

An efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity and reduces the execution time by more than 80% compared to an exact method, while achieving optimal solutions.

Abstract

This study aims to optimize vehicular user association to base stations in a mobile network. We propose an efficient heuristic solution that considers the base station average handover frequency, the channel quality indicator, and bandwidth capacity. We evaluate this solution using real-world base station locations from São Paulo, Brazil, and the SUMO mobility simulator. We compare our approach against a state of the art solution which uses route prediction, maintaining or surpassing the provided quality of service with the same number of handover operations. Additionally, the proposed solution reduces the execution time by more than 80\% compared to an exact method, while achieving optimal solutions.
Paper Structure (9 sections, 6 equations, 5 figures, 3 tables, 3 algorithms)

This paper contains 9 sections, 6 equations, 5 figures, 3 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Model
  4. Exact Approach
  5. Heuristic Approach
  6. Results
  7. Heuristic Performance
  8. Mobility Simulation Scenario
  9. Conclusion

Figures (5)

  • Figure 1: Simulation flowchart.
  • Figure 2: Simulated vehicular UE route in São Paulo city, Brazil, considering real-world eNB locations.
  • Figure 3: The allocation results for the ILS-VND (outer) and Ahmadi et al. (inner) models.
  • Figure 4: User link RSRQ value through the route.
  • Figure 5: The average RSRQ value for each instance's route.