In a distributed wireless network with a large number of nodes, competitive access of nodes may result in the deterioration of throughput and energy. Therefore, in this paper we propose an intelligent access algorithm based on the mean field game (MFG). First, we formulate the competitive access process between nodes as a game Query Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary." process by a stochastic differential game model, which maximizes the energy efficiency of nodes and obtain the optimal behavior strategy while meeting the requirements of channel access. However, as the number of nodes increases, the dimension of the matrix used to characterize the interaction between nodes becomes too large, which increases the complexity of the solution procedure. Therefore, we introduce the MFG and the interaction between nodes can be approximately transformed into the interaction between nodes and the mean field, which not only reduces the complexity, but also reduces the computational overhead. In addition, the HJB-FPK equation is solved to obtain the Nash equilibrium of the MFG. Finally, a backoff strategy based on the Markov model is proposed, and the node obtains the corresponding backoff strategy according to the network situation and its own state. Simulation results show that the proposed algorithm has good performances on optimizing network throughput and energy efficiency for a large scale multi-hop wireless network.

在具有大量节点的分布式无线网络中，节点的竞争接入可能导致吞吐量和能量的恶化。因此，在本文中，我们提出了一种基于平均场博弈（MFG）的智能访问算法。首先，我们将节点之间的竞争访问过程制定为游戏查询文本=“请检查并确认作者及其各自的隶属关系已被正确识别并在必要时进行修改。” 采用随机微分博弈模型进行处理，最大化节点能量效率，在满足通道接入要求的同时获得最优行为策略。但是，随着节点数量的增加，用来表征节点间交互的矩阵的维数变得太大，这增加了求解过程的复杂性。因此，我们引入MFG，节点间的交互可以近似地转化为节点与平均场的交互，不仅降低了复杂度，而且降低了计算开销。此外，求解 HJB-FPK 方程以获得 MFG 的纳什平衡。最后提出了一种基于马尔科夫模型的退避策略，节点根据网络情况和自身状态得到相应的退避策略。仿真结果表明，该算法在优化大规模多跳无线网络的网络吞吐量和能量效率方面具有良好的性能。我们引入MFG，节点间的交互可以近似地转化为节点与平均场的交互，不仅降低了复杂度，而且降低了计算开销。此外，求解 HJB-FPK 方程以获得 MFG 的纳什平衡。最后提出了一种基于马尔科夫模型的退避策略，节点根据网络情况和自身状态得到相应的退避策略。仿真结果表明，该算法在优化大规模多跳无线网络的网络吞吐量和能量效率方面具有良好的性能。我们引入MFG，节点间的交互可以近似地转化为节点与平均场的交互，不仅降低了复杂度，而且降低了计算开销。此外，求解 HJB-FPK 方程以获得 MFG 的纳什平衡。最后提出了一种基于马尔科夫模型的退避策略，节点根据网络情况和自身状态得到相应的退避策略。仿真结果表明，该算法在优化大规模多跳无线网络的网络吞吐量和能量效率方面具有良好的性能。求解 HJB-FPK 方程以获得 MFG 的纳什平衡。最后提出了一种基于马尔科夫模型的退避策略，节点根据网络情况和自身状态得到相应的退避策略。仿真结果表明，该算法在优化大规模多跳无线网络的网络吞吐量和能量效率方面具有良好的性能。求解 HJB-FPK 方程以获得 MFG 的纳什平衡。最后提出了一种基于马尔科夫模型的退避策略，节点根据网络情况和自身状态得到相应的退避策略。仿真结果表明，该算法在优化大规模多跳无线网络的网络吞吐量和能量效率方面具有良好的性能。