In Wireless Local Area Networks (WLANs), Access point (AP) transmit power influences (i) received signal quality for users and thus user throughput, (ii) user association and thus load across APs and (iii) AP coverage ranges and thus interference in the network. Despite decades of academic research, transmit power levels are still, in practice, statically assigned to satisfy uniform coverage objectives. Yet each network comes with its unique distribution of users in space, calling for a power control that adapts to users' probabilities of presence, for example, placing the areas with higher interference probabilities where user density is the lowest. Although nice on paper, putting this simple idea in practice comes with a number of challenges, with gains that are difficult to estimate, if any at all. This paper is the first to address these challenges and evaluate in a production network serving thousands of daily users the benefits of a user-aware transmit power control system. Along the way, we contribute a novel approach to reason about user densities of presence from historical IEEE 802.11k data, as well as a new machine learning approach to impute missing signal-strength measurements. Results of a thorough experimental campaign show feasibility and quantify the gains: compared to state-of-the-art solutions, the new system can increase the median signal strength by 15dBm, while decreasing airtime interference at the same time. This comes at an affordable cost of a 5dBm decrease in uplink signal due to lack of terminal cooperation.

翻译：在无线局域网（WLAN）中，接入点（AP）的发射功率会影响：(i)用户的接收信号质量进而影响用户吞吐量，(ii)用户关联进而影响AP间的负载分布，(iii)AP覆盖范围进而影响网络中的干扰。尽管已有数十年的学术研究，但在实际应用中，发射功率仍采用静态分配方式以满足均匀覆盖目标。然而，每个网络都拥有独特的用户空间分布特征，这要求功率控制能够自适应于用户存在概率——例如，将高干扰概率区域布置在用户密度最低的位置。这一理念在理论层面虽显合理，但付诸实践时面临诸多挑战，且其增益（若存在）难以评估。本文首次针对这些挑战展开研究，并在一个服务数千名日常用户的真实生产网络中，评估了面向用户的发射功率控制系统的实际效益。在此过程中，我们提出了基于历史IEEE 802.11k数据推断用户存在密度的新型推理方法，以及用于补全缺失信号强度测量的新型机器学习方法。通过详尽的实验验证，我们证明了该系统的可行性并量化了其增益：与现有最优方案相比，新系统可将中位信号强度提升15dBm，同时降低空中接口干扰时间。该性能提升的代价是，由于终端缺乏协作，上行链路信号强度会降低5dBm。