多频段移动网络中预测辅助的用户关联和负载均衡
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks
摘要：
蜂窝网络正变得越来越异构，具有更高的基站（BS）密度和更多的频段，使得BS选择和频带分配在速率和覆盖方面成为关键决策。在本文中，我们分解的移动性感知的用户关联任务（i）预测的用户速率，然后（ii）凸效用最大化的用户关联帐户的BS负载和切换开销的影响。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，训练递归深度神经网络以可靠地预测移动用户的未来速率。基于预测，控制器优化的关联决策，以最大限度地提高服务率为基础的网络效用使用我们的计算效率（速度100倍与通用凸解算器）算法的基础上的弗兰克-沃尔夫方法。使用由Meta开发的工业级网络模拟器，我们表明，所提出的模型预测控制（MPC）方法相比传统的基于信号强度的关联提高了3.5倍的第5百分位服务率，相比切换不可知策略减少了7倍的中间值，并实现了接近天才辅助方案的服务率。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明显更高（需要的训练数据减少100倍），并且可以很好地推广到不同的用户丢弃场景。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
