Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage. In this paper, we decompose the mobility-aware user association task into (i) forecasting of user rate and then (ii) convex utility maximization for user association accounting for the effects of BS load and handover overheads. Using a linear combination of normalized mean-squared error and normalized discounted cumulative gain as a novel loss function, a recurrent deep neural network is trained to reliably forecast the mobile users' future rates. Based on the forecast, the controller optimizes the association decisions to maximize the service rate-based network utility using our computationally efficient (speed up of 100x versus generic convex solver) algorithm based on the Frank-Wolfe method. Using an industry-grade network simulator developed by Meta, we show that the proposed model predictive control (MPC) approach improves the 5th percentile service rate by 3.5x compared to the traditional signal strength-based association, reduces the median number of handovers by 7x compared to a handover agnostic strategy, and achieves service rates close to a genie-aided scheme. Furthermore, our model-based approach is significantly more sample-efficient (needs 100x less training data) compared to model-free reinforcement learning (RL), and generalizes well across different user drop scenarios.

中文翻译：

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多频段移动网络中的预报员辅助用户关联和负载平衡

随着基站 (BS) 密度越来越高和频段越来越多，蜂窝网络变得越来越异构，这使得 BS 选择和频段分配在速率和覆盖范围方面成为关键决策。在本文中，我们将移动感知用户关联任务分解为 (i) 用户速率预测和 (ii) 考虑 BS 负载和切换开销影响的用户关联的凸效用最大化。使用归一化均方误差和归一化贴现累积增益的线性组合作为新的损失函数，训练循环深度神经网络以可靠地预测移动用户的未来费率。根据预测，控制器使用我们基于 Frank-Wolfe 方法的计算高效（与通用凸求解器相比加速 100 倍）算法优化关联决策，以最大化基于服务速率的网络效用。使用 Meta 开发的工业级网络模拟器，我们表明，与传统的基于信号强度的关联相比，所提出的模型预测控制 (MPC) 方法将第 5 个百分位服务率提高了 3.5 倍，将切换的中位数减少了 7 倍与切换不可知策略相比，并实现接近精灵辅助方案的服务率。此外，与无模型强化学习 (RL) 相比，我们基于模型的方法的样本效率显着提高（需要的训练数据减少 100 倍），并且可以很好地概括不同的用户丢弃场景。