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Intelligent Agent for Resource Allocation from Mobile Infrastructure to Vehicles in Dynamic Environments Scalable on
Renato Cumbal1, Berenice Arguero1, Germán V Arévalo1
1Carrera de Telecomunicaciones, Universidad Politécnica Salesiana, Quito 170525, Ecuador.
This study introduces an intelligent framework for Vehicle-to-Infrastructure (V2I) communications, optimizing Road-Side Unit (RSU) placement and radio-resource allocation. The system achieves over 90% coverage using minimal RSUs and dynamically manages resources for efficient intelligent transportation systems.
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Area of Science:
- Intelligent Transportation Systems
- Wireless Communications
- Network Optimization
Background:
- Urban mobility faces increasing complexity, necessitating advanced communication frameworks.
- Existing Vehicle-to-Infrastructure (V2I) systems require efficient resource allocation and infrastructure deployment strategies.
Purpose of the Study:
- To develop an intelligent optimization and resource-allocation framework for V2I communications.
- To enhance vehicular coverage and radio-resource efficiency in urban environments.
Main Methods:
- Macroscopic mobility analysis integrated with Integer Linear Programming (ILP) for optimal Road-Side Unit (RSU) placement.
- Q-learning-based Smart Generic Network Controller (SGNC) for dynamic radio-resource allocation.
- Simulations in a realistic georeferenced urban scenario.
Main Results:
- ILP model activated only 2.9% of RSUs, ensuring over 90% vehicular coverage.
- SGNC demonstrated stable resource allocation, managing 10 antennas and 120 resources.
- Dynamic reallocation mechanism maintained efficiency above 70% system capacity.
Conclusions:
- The proposed framework offers a scalable solution for V2I deployment in intelligent transportation systems.
- Dynamic resource allocation significantly improves efficiency and coverage consistency compared to static methods.
- The intelligent framework effectively addresses the complexities of modern urban mobility.