Channel Estimation Algorithms for Throughput Stability in Vehicular Communication Corridors: Comparative Analysis

Abstract

The rapid proliferation of intelligent transportation systems heavily relies on robust and reliable vehicular communication networks. However, the highly dynamic nature of vehicular environments, characterized by rapid topological changes and severe multipath fading, poses significant challenges to maintaining stable data throughput. This paper presents a comprehensive comparative analysis of various channel estimation algorithms and their direct impact on throughput stability within vehicular communication corridors. By evaluating traditional techniques such as Least Squares and Minimum Mean Square Error estimators alongside advanced, data-driven machine learning approaches, this study identifies the critical trade-offs between computational complexity, estimation accuracy, and overall network performance. The analysis focuses on high-mobility scenarios where Doppler shifts significantly degrade channel state information reliability. Through extensive simulation modeling of an orthogonal frequency division multiplexing based communication system, performance metrics including normalized mean square error and effective throughput are rigorously assessed across multiple vehicular velocities. The findings indicate that while traditional estimators struggle under high-speed conditions due to rapid channel aging, adaptive machine learning frameworks provide superior resilience, maintaining throughput stability even at elevated velocities. This research provides essential insights for the design and optimization of next-generation vehicle-to-everything communication protocols, ensuring the stringent reliability and latency requirements of autonomous driving applications are met efficiently.