Wireless local area network (WLAN) access points (APs) are being deployed in high density to improve coverage and throughput. The emerging multiple-input multiple-output (MIMO) implementation for uplink (UL) transmissions promises high per-user throughput and improved aggregate network throughput. However, the high throughput potential of dense UL-MIMO WLAN is impaired by multiple access channel interference and high contention among densely distributed user stations (STAs). We investigate the problem of actualizing the throughput potential of UL-MIMO in high density WLANs via user-AP association. Since user-AP association influences interference and STA contention, a method to optimally distribute STAs among APs is proposed to maximize aggregate users' throughput utility. This problem is transformed into a graph matching problem with the throughput utility function as the graph edge weights. The graph matching problem is solved as a combinatorial problem using a modified classical Kuhn-Munkres algorithm. A dynamic implementation of the proposed algorithm is used to periodically update user-AP associations when there are changes in the network due to new entrants and/or user mobility. Simulated dense UL-MIMO WLAN scenarios reveal that the proposed scheme achieves an average of $36.9 \%$, $33.5 \%$, $20.4 \%$ and $11.3 \%$ gains over the default strongest signal first (SSF) association scheme used in conventional WLAN, Greedy [14], SmartAssoc [13] and best performance first (BPF) [5] algorithms, respectively.

翻译：为提高覆盖范围和吞吐量，无线局域网接入点正以高密度方式部署。新兴的多输入多输出上行链路传输技术有望实现高单用户吞吐量并提升网络总吞吐量。然而，密集上行MIMO无线局域网的高吞吐量潜力受到多址信道干扰和密集分布的用户站点间高竞争度的制约。本研究通过用户-AP关联机制，探讨实现高密度无线局域网上行MIMO吞吐量潜力的方法。由于用户-AP关联会影响干扰和用户站点竞争，本文提出一种在接入点间优化分配用户站点的方法，以最大化用户总吞吐量效用。该问题被转化为以吞吐量效用函数为图边权重的图匹配问题，并采用改进的经典Kuhn-Munkres算法作为组合优化问题进行求解。当网络因新用户加入或用户移动性发生变化时，采用所提算法的动态实现方案定期更新用户-AP关联。对密集上行MIMO无线局域网场景的仿真表明：相较于传统无线局域网默认采用的最强信号优先关联方案、Greedy [14]、SmartAssoc [13] 以及最佳性能优先 [5] 算法，所提方案分别实现了平均 $36.9 \%$、$33.5 \%$、$20.4 \%$ 和 $11.3 \%$ 的性能增益。