Joint clustering and power allocation for the cross roads congestion scenarios in cooperative vehicular networks

Both clustering and cluster-head vehicles (CHVs) cooperative communication have been employed for reducing traffic congestion to improve road traffic efficiency in cooperative vehicular networks. In this paper, an iterative optimization k-means clustering algorithm with lower complexity than previous algorithms is proposed. It can automatically generate multiple clusters according to the number of vehicles and quickly find the CHVs by avoiding delays caused by complex calculations. Moreover, a new optimization power allocation strategy with bidirectional incremental hybrid decode-amplify-forward protocol focusing on reducing the total power consumption of CHVs is proposed. This strategy can set the signal to noise ratio threshold as the critical point for selecting dynamically the bidirectional incremental amplify-and-forward or decoding and forwarding protocol with a lower outage probability to transmit information. Thus, the proposed power allocation strategy is capable of minimizing the total transmission power while ensuring a lower outage probability than previous approaches. Finally, the numerical results are provided for corroborating the theoretical results and demonstrate the efficiency of the proposed approaches. Note that through the numerical simulations we can find the critical point of the outage probability for the aforementioned protocols under different relay locations. This assists vehicles to select 'relays' with the optimal cooperative position for vehicular cooperative communication system.