Value-Decomposition Networks based Distributed Interference Control in Multi -platoon Groupcast

Platooning is considered one of the most representative 5G use cases. Due to the small spacing within the platoon, the platoon needs more reliable transmissions to guarantee driving safety while improving fuel and driving efficiency. However, efficient resource allocation between platoons has been a challenge, especially considering that the channel and power selected by each platoon will affect other platoons. Therefore, platoons need to coordinate with each other to ensure the groupcast quality of each platoon. To solve these challenges, the multi-platoon groupcast is modeled by Markov game firstly, then a distributed resource allocation algorithm based on Value-Decomposition Networks is proposed. Our scheme utilizes the historical data of each platoon for centralized training. At the same time, the training burden is decreased by sharing the neural network parameters between platoons. Then during distributed execution, platoon only need their local observations to make decisions. Simulation results show that the proposed algorithm has excellent convergence. Compared with another multi-agent algorithm (MARL) and the baseline algorithm, our proposed solution can dramatically reduce the probability of platoon groupcast failure and improve the quality of platoon groupcast.