Computational Experiments and Comparative Analysis of Signal Detection Algorithms in Vehicular Ad Hoc Networks

In the era of rapid development of vehicular ad hoc networks (VANETs), ensuring the reliability and security of vehicle-to-vehicle communication has become a top priority. This paper comprehensively analyzes the performance of various signal detection algorithms in different scenarios. To intelligently choose different signal detection algorithms in the context of VANETs, the study covers diverse scenarios such as urban environments, rural areas, highways, parking lots, and mountainous regions, aiming to capture subtle variations in the performance of different signal detection algorithms across these scenarios. The paper employs strict performance metrics, such as bit error rate and algorithmic complexity, to quantify and compare the performance of different signal detection algorithms. The focus is on the role of signal detection algorithms in achieving parallel intelligence in VANETs, including the simultaneous processing of signals from multiple vehicles to enhance overall network efficiency and reliability. This research holds significance by providing insights into the strengths and limitations of signal detection algorithms in VANETs, guiding their development for efficient and accurate performance, thereby contributing to academic understanding and informing decision-making in the automotive industry and intelligent transportation systems.