多目标联合优化的车联网动态资源分配算法

To address the problem of communication transmission performance degradation resulting from high dynamic uncertainty of channels and multi-user interference in the Internet of vehicles(IoV),a multi-objective joint optimization resource allocation algorithm based on multi-agent enhanced double deep Q network (EDDQN)is proposed. First,considering the vehicle motion and the time-varying channel characteristics,a resource allocation decision model for the joint optimization of spectrum sharing and power control under multi-user interference is established to minimize the cost,which is the weighted sum of network delay and energy consumption,under the constraints of delay and reliability. Then,the model is transformed into a Markov decision process(MDP). The priority experience replay and the multi-step learning are introduced into the double deep Q-network(DDQN)with centralized training and distributed execution to optimize the sub-band and power allocation strategies for vehicle to vehicle(V2V)links. The results demonstrate that the proposed algorithm has good convergence. Compared with the benchmark algorithms,the proposed algorithm reduces the cost by over 8% and increases the transmission success rate by over 19% under different loads,effectively improving the communication transmission performance.