在均匀传播的理想假设下，无蜂窝大规模 MIMO（CF-mMIMO）通过有效地将每个用户包围在其服务接入点（AP）集合中，从而在整个网络上提供均匀的高吞吐量。然而，在现实的非均匀城市传播环境中，难以持续选择良好的有限服务 AP 集合，导致吞吐量显著下降，尤其是对最差服务用户（原“小区边缘”用户）的影响更为严重。为了在现实城市网络中恢复可扩展 CF-mMIMO 的均匀良好性能，我们构建了一个新的多目标优化问题，旨在通过最大化总数据速率来实现高吞吐量，通过最大化每用户吞吐量的 Jain 公平性指数来实现均匀吞吐量，并通过最小化服务 AP 集合大小来实现可扩展性。随后，我们提出了 UnifSrv AP 选择算法以解决该优化问题，其中包括基于深度强化学习（DRL）的算法 UnifSrv-DRL 和启发式算法 UnifSrv-heu。我们在现实的城市网络分布、传播和移动模式下对可扩展 CF-mMIMO 进行了全面的性能评估。结果表明，UnifSrv 显著优于先前的基准 AP 选择方案，并首次在非均匀城市传播条件下实现了 CF-mMIMO 的均匀高吞吐量。重要的是，我们的启发式算法达到了与 DRL 算法相当的吞吐量，但复杂度低了几个数量级。因此，我们首次提出了一种实用的 AP 选择算法，使 CF-mMIMO 在现实城市网络中具有可行性。

Under the ideal assumption of uniform propagation, cell-free massive MIMO (CF-mMIMO) provides uniformly high throughput over the network by effectively surrounding each user with its serving access point (AP) set. However, in realistic non-uniform urban propagation environments, it is difficult to consistently select good limited serving AP sets, resulting in significantly degraded throughput, especially for the worst-served (formerly "cell-edge") users. To restore the uniformly good performance of scalable CF-mMIMO in realistic urban networks, we formulate a novel multi-objective optimization problem to jointly achieve high throughput by maximizing the sum data rate, uniform throughput by maximizing Jain's fairness index of the throughput per user, and scalability by minimizing the serving AP set size. We then propose the UnifSrv AP selection algorithms to solve this optimization problem, consisting of a deep reinforcement learning (DRL)-based algorithm UnifSrv-DRL and a heuristic algorithm UnifSrv-heu. We conduct a comprehensive performance evaluation of scalable CF-mMIMO under realistic urban network distributions, propagation, and mobility patterns. Our results show that UnifSrv significantly outperforms the prior benchmark AP selection schemes, and for the first time achieves uniformly high throughput of CF-mMIMO under non-uniform urban propagation. Importantly, our heuristic algorithm achieves equivalent throughput to our DRL one, but with orders of magnitude lower complexity. We thus for the first time propose a practical AP selection algorithm that makes CF-mMIMO viable in realistic urban networks.