With the rapid advancement of the automotive industry,the demand for vehicle-centric communication continues to grow.In vehicular ad hoc networks(VANETs),the interactions among vehicles can be modeled as a social netw...With the rapid advancement of the automotive industry,the demand for vehicle-centric communication continues to grow.In vehicular ad hoc networks(VANETs),the interactions among vehicles can be modeled as a social network.This paper explores the social dynamics of such networks by analyzing the eigenvector centrality of vehicles and classifying communication levels based on their centrality rankings.To support this analysis,a communication system is designed using a multiple-input multiple-output(MIMO)orthogonal frequency division multiplexing framework,incorporating the derived communication levels.Within this system,a particle swarm optimization(PSO)algorithm is employed to optimize a radial basis function(RBF)neural network for channel estimation.This approach significantly improves performance,achieving a bit error rate(BER)below 10−4 at relatively low signal-to-noise ratios(SNRs).Moreover,the proposed method enables the system to approach the theoretical channel capacity limit under low SNR conditions.The communication level detection method presented in this work also achieves 100%accuracy across various signal detection techniques.Overall,the proposed signal detection framework offers promising potential for enhancing the performance and reliability of future vehicular communication systems.展开更多
基金supported by the Open Fund Project of Key Laboratory of Marine Environmental Survey Technology and Application,Ministry of Natural Resources(No.MESTA-2024-B004).
文摘With the rapid advancement of the automotive industry,the demand for vehicle-centric communication continues to grow.In vehicular ad hoc networks(VANETs),the interactions among vehicles can be modeled as a social network.This paper explores the social dynamics of such networks by analyzing the eigenvector centrality of vehicles and classifying communication levels based on their centrality rankings.To support this analysis,a communication system is designed using a multiple-input multiple-output(MIMO)orthogonal frequency division multiplexing framework,incorporating the derived communication levels.Within this system,a particle swarm optimization(PSO)algorithm is employed to optimize a radial basis function(RBF)neural network for channel estimation.This approach significantly improves performance,achieving a bit error rate(BER)below 10−4 at relatively low signal-to-noise ratios(SNRs).Moreover,the proposed method enables the system to approach the theoretical channel capacity limit under low SNR conditions.The communication level detection method presented in this work also achieves 100%accuracy across various signal detection techniques.Overall,the proposed signal detection framework offers promising potential for enhancing the performance and reliability of future vehicular communication systems.
