A reinforcement learning approach for cost- and energy-aware mobile data offloading
A reinforcement learning approach for cost- and energy-aware mobile data offloading
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DOI：
10.1109/apnoms.2016.7737203
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发表时间：
2016-11
期刊：
影响因子：
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通讯作者：
Cheng Zhang;Bo Gu;Zhi Liu;K. Yamori;Y. Tanaka
中科院分区：
文献类型：
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作者：
Cheng Zhang;Bo Gu;Zhi Liu;K. Yamori;Y. Tanaka
随着对移动的数据的需求的快速增长，移动的网络运营商正试图通过部署WiFi热点来卸载其移动的业务来扩展无线网络容量。然而，这些以网络为中心的方法通常不能满足移动的用户（MU）的兴趣。MU考虑许多问题来决定是否将其业务卸载到互补WiFi网络。在本文中，我们研究的WiFi卸载问题，从MU的角度考虑延迟容忍的流量，货币成本，能源消耗以及MU的移动模式的可用性。我们首先制定的WiFi卸载问题作为一个有限时域离散时间马尔可夫决策过程（FDTMDP）与已知MU的移动模式，并提出了一个动态规划的卸载算法。由于MU的移动模式可能事先不知道，我们然后提出了一个基于强化学习的卸载算法，它可以很好地工作与未知的MU的移动模式。大量的模拟进行验证我们提出的卸载算法。
With rapid increases in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying WiFi hotspots to offload their mobile traffic. However, these network-centric methods usually do not fulfill interests of mobile users (MUs). MUs consider many problems to decide whether to offload their traffic to a complementary WiFi network. In this paper, we study the WiFi offloading problem from MU's perspective by considering delay-tolerance of traffic, monetary cost, energy consumption as well as the availability of MU's mobility pattern. We first formulate the WiFi offloading problem as a finite-horizon discrete-time Markov decision process (FDTMDP) with known MU's mobility pattern and propose a dynamic programming based offloading algorithm. Since MU's mobility pattern may not be known in advance, we then propose a reinforcement learning based offloading algorithm, which can work well with unknown MU's mobility pattern. Extensive simulations are conducted to validate our proposed offloading algorithms.