论文标题
基于价值分解网络的分布式干扰控制
Value-Decomposition Networks based Distributed Interference Control in Multi-platoon Groupcast
论文作者
论文摘要
排被认为是最具代表性的5G用例之一。由于排内间距很小，排需要更可靠的传输，以确保驾驶安全性，同时提高燃料和驾驶效率。但是，排之间的有效资源分配是一个挑战，尤其是考虑到每个排选择的渠道和功率会影响其他排。因此，排需要相互协调，以确保每个排的组播放质量。为了解决这些挑战，我们将多播资源选择问题建模为马尔可夫游戏，然后根据价值分解网络提出分布式资源分配算法。我们的计划利用每个排的历史数据进行集中培训。在分布式执行中，代理只需要其本地观察来做出决定。同时，我们通过共享神经网络参数来减轻培训负担。仿真结果表明，所提出的算法具有出色的收敛性。与另一种多代理算法（MARL）和随机算法相比，我们提出的解决方案可以大大降低排组播放失败的可能性并提高排组播放的质量。
Platooning is considered one of the most representative 5G use cases. Due to the small spacing within the platoon, the platoon needs more reliable transmission 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, we model the multi-platoon resource selection problem as Markov games and then propose a distributed resource allocation algorithm based on Value-Decomposition Networks. Our scheme utilizes the historical data of each platoon for centralized training. In distributed execution, agents only need their local observations to make decisions. At the same time, we decrease the training burden by sharing the neural network parameters. Simulation results show that the proposed algorithm has excellent convergence. Compared with another multi-agent algorithm (MARL) and random algorithm, our proposed solution can dramatically reduce the probability of platoon groupcast failure and improve the quality of platoon groupcast.
