Optimizing Channel Resource Allocation in Dynamic Vehicular Environments for Enhanced Throughput

In the rapidly evolving domain of intelligent transportation systems, the vehicular network is pivotal for achieving vehicle intelligence and road safety. This paper addresses the critical issue of optimizing channel resource allocation in dynamic vehicular environments to enhance communication stability and throughput. The proposed solution leverages a mixed-integer nonlinear programming (MINLP) framework to dynamically allocate subchannels and power among vehicles, ensuring high throughput and reliable communication. The algorithm integrates both V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communication types, adapting to the complex and changing road conditions. By utilizing the Link Expiry Time (LET) strategy and MIMO communication techniques, the approach ensures timely updates and robust data transmission. Through extensive simulations, the proposed method demonstrates significant improvements in system sum rate and transmission success rate compared to traditional schemes. This research advances intelligent transportation systems by providing a scalable and effective solution to meet the increasing demands of vehicular communications.