Coordinating mixed fleets of massive vehicles under stringent delay constraints is a central scalability bottleneck in next-generation mobile computing networks, especially when passenger cars, freight trucks, and autonomous vehicles share the same radio and multi-access edge computing (MEC) infrastructure. Heterogeneous mean field games (HMFG) are a principled framework for this setting, but a fundamental design question remains open: how many agent types should be used for a fleet of size $N$? The difficulty is a two-sided trade-off that existing theory does not resolve: using more types improves heterogeneity representation, but it reduces per-class sample size and weakens the mean-field approximation accuracy. This paper resolves that trade-off through an explicit $\varepsilon$-Nash error decomposition, a closed-form type-selection law, a heterogeneity-aware equilibrium solver, and a robust extension to time-varying LEO backhaul dynamics. For the 1D queue state space, the optimal type count satisfies $K^*(N)=Θ(N^{1/3})$; for the joint queue-channel model ($d=2$), the scaling becomes $K^*(N)=Θ(N^{1/5})$ with logarithmic correction. The unified formula $K^*(N)=Θ(N^{α/(α+β)})$ provides dimension-dependent design guidance, reducing type granularity to a principled, set-once system parameter rather than a per-deployment tuning burden. Experiments validate the 1D scaling law with empirical slope $0.334 \pm 0.004$, achieve $2.3\times$ faster PDHG convergence at $K=5$, and deliver up to $29.5\%$ lower delay and $60\%$ higher throughput than homogeneous baselines. Unlike model-free DRL methods whose training complexity scales with the state-action space, the proposed HMFG solver has per-iteration complexity $O(K^2 N_q N_t)$ independent of fleet size $N$, making it suitable for large-scale mobile edge computing deployment.

翻译：在严格的延迟约束下协调大规模混合车队，是下一代移动计算网络的核心可扩展性瓶颈，尤其是当乘用车、货运卡车和自动驾驶车辆共享同一无线电和多接入边缘计算（MEC）基础设施时。异构平均场博弈（HMFG）为此场景提供了理论化框架，但一个基础设计问题仍然悬而未决：对于规模为$N$的车队，应使用多少种智能体类型？其难点在于现有理论未能解决的双向权衡：使用更多类型可提升异质性表征能力，但会减少每类样本量并削弱平均场近似精度。本文通过显式$\varepsilon$-纳什误差分解、闭式类型选择定律、异质性感知均衡求解器，以及针对时变LEO回程动态的鲁棒扩展，解决了这一权衡问题。对于一维队列状态空间，最优类型数满足$K^*(N)=Θ(N^{1/3})$；对于联合队列-信道模型（$d=2$），缩放关系变为$K^*(N)=Θ(N^{1/5})$并伴随对数修正。统一公式$K^*(N)=Θ(N^{α/(α+β)})$提供了维度依赖的设计指导，将类型粒度转化为一个原则性、一次设定的系统参数，而非每次部署的调优负担。实验验证了实测斜率为$0.334 \pm 0.004$的一维缩放律，在$K=5$时实现了$2.3$倍更快的PDHG收敛，相比同构基线方案，延迟降低高达$29.5\%$，吞吐量提升高达$60\%$。与训练复杂度随状态-动作空间扩展的无模型DRL方法不同，本文提出的HMFG求解器每次迭代复杂度为$O(K^2 N_q N_t)$，与车队规模$N$无关，使其适用于大规模移动边缘计算部署。