Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-the-leader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.

中文翻译：

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无线边缘缓存中的在线内容流行度预测与学习

提前缓存流行内容是未来无线通信中实现低延迟和降低回程成本的重要技术。考虑将基站分布为泊松点过程的网络，获得最佳内容放置缓存概率以最大化已知内容流行度 (CP) 配置文件的平均成功概率 (ASP)，该配置文件在实践中随时间变化且事先未知. 在本文中，我们首先提出了两种预测 CP 的在线预测 (OP) 方法，即流行度预测模型 (PPM) 和格拉斯曼预测模型 (GPM)，其中线性预测的无约束系数是通过求解约束非负最小正方形。为了降低每轮在线较高的计算复杂度，有两种在线学习 (OL) 方法，即：受 OP 模型的启发，提出了加权跟随领导者和加权跟随正则化领导者。在 OP 中，ASP 差异（即通过预测获得的 ASP 与已知内容流行度之间的差距）是有界的，而在 OL 中，获得了亚线性 MSE 后悔和线性 ASP 后悔界限。使用 MovieLens 数据集，模拟验证了 OP 方法更适合 MSE 和 ASP 差异最小化，而 OL 方法在最小化 MSE 和 ASP 遗憾方面表现良好。