A Unified Framework for Adaptive Beamforming and State Estimation in Dynamic Multi-Lane V2V Networks

This paper presents a Vehicle-to-Vehicle (V2V) communication modeling framework that addresses the challenges of reliable state estimation and beamforming control in dynamic, multi-lane road environments. By integrating an extended Unscented Kalman Filter (UKF) with adaptive process and measurement noise models, the proposed approach accurately tracks vehicle trajectories under abrupt speed variations, frequent lane changes, and adverse weather conditions. A Markov chain-based lane-switching mechanism enables realistic multi-lane traffic simulations with smooth centerline trajectories spanning straight and curved road segments. To further enhance robustness, an adaptive Minimum Variance Distortionless Response (MVDR) beamforming scheme compensates for beam misalignment and mitigates interference, thereby significantly improving the Signal-to-Interference-Plus-Noise Ratio (SINR). The results demonstrate that the framework not only offers improved positioning accuracy but also achieves reliable communication performance compared to conventional methods, reinforcing its effectiveness in complex vehicular scenarios.