A Collaborative Learning Based Approach for Parameter Configuration of Cellular Networks
A Collaborative Learning Based Approach for Parameter Configuration of Cellular Networks
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DOI：
10.1109/infocom.2019.8737657
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发表时间：
2019-04
期刊：
影响因子：
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通讯作者：
Jie Chuai;Zhitang Chen;Guochen Liu;Xueying Guo;Xiaoxiao Wang;Xin Liu;Chongming Zhu;Feiyi Shen
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文献类型：
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作者：
Jie Chuai;Zhitang Chen;Guochen Liu;Xueying Guo;Xiaoxiao Wang;Xin Liu;Chongming Zhu;Feiyi Shen
蜂窝网络的性能在很大程度上取决于其网络参数的配置。当前参数配置的实践很大程度上依赖于专家经验，这往往不是最优的、耗时的且容易出错。因此，希望通过基于学习的方法自动化此过程以提高准确性和效率。然而，此类方法需要解决实际运营网络中的几个挑战：缺乏多样化的历史数据、网络运营商设定的实验预算有限以及高度复杂且未知的网络性能函数。为了应对这些挑战，我们提出了一种协作学习方法，利用来自不同单元的数据来提高学习效率并提高网络性能。具体来说，我们将该问题表述为可转移的上下文强盗问题，并证明通过转移学习，可以显着减少后悔界限。基于理论结果，我们进一步开发了一种实用算法，将单元格的策略分解为使用所有单元格数据学习的通用同质策略和捕获每个单元格异构行为的特定于单元格的策略。我们通过使用真实网络数据构建的模拟器评估我们提出的算法，并证明与基线相比更快的收敛。更重要的是，还在由1700多个小区组成的真实城域蜂窝网络上进行了现场测试，为期两周优化了五个参数。我们提出的算法显示性能显着提高了 20%。
Cellular network performance depends heavily on the configuration of its network parameters. Current practice of parameter configuration relies largely on expert experience, which is often suboptimal, time-consuming, and error-prone. Therefore, it is desirable to automate this process to improve the accuracy and efficiency via learning-based approaches. However, such approaches need to address several challenges in real operational networks: the lack of diverse historical data, a limited amount of experiment budget set by network operators, and highly complex and unknown network performance functions. To address those challenges, we propose a collaborative learning approach to leverage data from different cells to boost the learning efficiency and to improve network performance. Specifically, we formulate the problem as a transferable contextual bandit problem, and prove that by transfer learning, one could significantly reduce the regret bound. Based on the theoretical result, we further develop a practical algorithm that decomposes a cell’s policy into a common homogeneous policy learned using all cells’ data and a cell-specific policy that captures each individual cell’s heterogeneous behavior. We evaluate our proposed algorithm via a simulator constructed using real network data and demonstrates faster convergence compared to baselines. More importantly, a live field test is also conducted on a real metropolitan cellular network consisting 1700+ cells to optimize five parameters for two weeks. Our proposed algorithm shows a significant performance improvement of 20%.