# Intelligent Agent for Resource Allocation from Mobile Infrastructure to Vehicles in Dynamic Environments Scalable on Demand

## TL;DR

This paper introduces an intelligent system for efficiently allocating communication resources between vehicles and infrastructure in urban environments.

## Contribution

The novel framework combines macroscopic mobility analysis, ILP for RSU placement, and Q-learning for dynamic resource allocation in V2I systems.

## Key findings

- The ILP model activates only 2.9% of RSUs while ensuring over 90% vehicular coverage.
- The SGNC manages 10 antennas and 120 resources efficiently, maintaining performance beyond 70% capacity.
- The proposed method outperforms static allocation in resource efficiency and coverage consistency under varying traffic.

## Abstract

This work addresses the increasing complexity of urban mobility by proposing an intelligent optimization and resource-allocation framework for Vehicle-to-Infrastructure (V2I) communications. The model integrates a macroscopic mobility analysis, an Integer Linear Programming (ILP) formulation for optimal Road-Side Unit (RSU) placement, and a Smart Generic Network Controller (SGNC) based on Q-learning for dynamic radio-resource allocation. Simulation results in a realistic georeferenced urban scenario with 380 candidate sites show that the ILP model activates only 2.9% of RSUs while guaranteeing more than 90% vehicular coverage. The reinforcement-learning-based SGNC achieves stable allocation behavior, successfully managing 10 antennas and 120 total resources, and maintaining efficient operation when the system exceeds 70% capacity by reallocating resources dynamically through the λ-based alert mechanism. Compared with static allocation, the proposed method improves resource efficiency and coverage consistency under varying traffic demand, demonstrating its potential for scalable V2I deployment in next-generation intelligent transportation systems.