Research on Communication and Perception Signal Detection Strategies for Intelligent Connected Vehicles

Intelligent connected vehicles have key problems such as signal interference, unbalanced resource allocation and multipath effect suppression in communication and perception signal detection. To solve these problems, this study proposes a system architecture based on joint communication and sensing. Through adaptive signal processing algorithms and collaborative optimization strategies, the joint encoding and dynamic resource allocation of communication data and perception signal are achieved. The simulation experiment results show that the proposed scheme improves the perception accuracy to 94.5 % in the static scene, reduces the communication bit error rate to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.1 \times 10^{-4}$</tex>, and increases the channel gain by 23 % through beamforming optimization in the dynamic scene. This research provides a feasible solution for efficient communication and precise perception of intelligent connected vehicles in complex environments.