Dense indoor WLANs increasingly rely on multiple access points (APs) operating over partially overlapping spectrum to support latency-sensitive applications. In such deployments, simultaneous transmissions across APs create co-channel and adjacent-channel interference, making scheduling decisions interdependent and directly impacting information freshness. Motivated by emerging software-defined WLAN architectures that enable centralized coordination, we study the problem of minimizing network-wide Age of Information (AoI) in multi-AP WLANs. Unlike classical AoI scheduling that runs at a single AP, each scheduling decision is now coupled across APs due to interference. This leads to a new class of combinatorial AoI control problems with action-dependent time evolution. We first derive a lower bound on the achievable AoI under arbitrary scheduling policies. We then design stationary randomized policies that have constant-factor optimality guarantees relative to this bound. Building on these insights, we develop a Lyapunov drift-based online policy for systems with action-dependent frame lengths, and establish constant-factor guarantees using new ratio-based drift analysis. To enable scalable implementation, we further show that per-frame scheduling admits efficient polynomial-time local-search approximations under a submodularity assumption. Simulations using realistic WLAN layouts demonstrate about 50% AoI reduction over distributed single AP baselines.

翻译：密集室内无线局域网（WLAN）日益依赖在部分重叠频谱上运行的多接入点（AP）来支持延迟敏感型应用。在此类部署中，AP间的并发传输会产生同信道与邻信道干扰，导致调度决策相互耦合，并直接影响信息新鲜度。受新兴软件定义WLAN架构（支持集中式协调）的启发，我们研究了多AP WLAN中最小化全网信息年龄（Age of Information, AoI）的问题。与经典的单AP AoI调度不同，此处每个调度决策因干扰而跨AP耦合，从而衍生出一类具有动作依赖时间演化的新型组合AoI控制问题。我们首先推导了任意调度策略下可实现AoI的下界，随后设计了相对于该界具有常数因子最优性保证的平稳随机策略。基于这些发现，我们针对动作依赖帧长度的系统提出了一种基于李雅普诺夫漂移的在线策略，并通过新型比例漂移分析建立了常数因子保证。为实现可扩展部署，我们进一步证明在子模性假设下，每帧调度可借助高效的多项式时间局部搜索近似实现。基于真实WLAN布局的仿真表明，与分布式单AP基准方案相比，所提方法可实现约50%的AoI降低。