Adaptive Resource Allocation Considering Power-Consumption Outage: A Deep Reinforcement Learning Approach
Adaptive Resource Allocation Considering Power-Consumption Outage: A Deep Reinforcement Learning Approach
复制标题
考虑功耗中断的自适应资源分配：深度强化学习方法
DOI：
10.1109/tvt.2023.3237730
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发表时间：
2023-06
影响因子：
6.8
通讯作者：
Yu Li
中科院分区：
文献类型：
--
作者：
Jia Luo;Qianbin Chen;Lun Tang;Zhicai Zhang;Yu Li
未来无线网络中移动的设备的主要特征是高数据速率。然而，新提出的功耗中断表明，高数据速率设备产生的热量对性能有影响。该通信研究了一种新的资源分配方案，考虑功耗中断。具体而言，基于智能手机中的热传递模型的分析，我们制定了问题，以联合分配下行链路功率和带宽，同时考虑功耗中断的影响。根据问题的动态特性，采用马尔可夫决策过程（MDP）对其进行建模，并利用动作空间的连续性，采用深度强化学习（DRL）算法--归一化优势函数（NAF）对问题进行处理。仿真结果证实了该方案的有效性，并在此基础上进一步讨论了该方案对不同业务的适用性。
A major feature of mobile devices in the future wireless network is the high data rate. However, the newly proposed power-consumption outage indicates that the heat generated by high data rate devices has an influence on the performance. This correspondence investigates a novel resource allocation scheme considering power-consumption outage. Specifically, based on the analysis of the heat transfer model in the smartphone, we formulate the problem to jointly allocate the downlink power and bandwidth while considering the impact of power-consumption outage. According to the dynamic feature of the problem, the Markov decision process (MDP) is utilized to model it. Furthermore, dut to the continuity of action space, the problem is handled by normalized advantage function (NAF), a deep reinforcement learning (DRL) algorithm. The effectiveness of the proposed scheme is corroborated in the simulation results on the basis of which its applicability for services with different features is further discussed.
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DOI：
10.23919/date.2019.8714831
发表时间：
2019-03
期刊：
2019 Design, Automation & Test in Europe Conference & Exhibition (DATE)
影响因子：
--
作者：
Ganapati Bhat;S. Gumussoy;Ümit Y. Ogras
通讯作者：
Ganapati Bhat;S. Gumussoy;Ümit Y. Ogras
影响因子：
9.3
作者：
Jing Yang;Xiaohu Ge;Yi Zhong
通讯作者：
Jing Yang;Xiaohu Ge;Yi Zhong
影响因子：
10.4
作者：
Yang J;Ge X;Thompson J;Gharavi H
通讯作者：
Gharavi H
DOI：
10.1109/glocom.2018.8647593
发表时间：
2018-12
期刊：
2018 IEEE Global Communications Conference (GLOBECOM)
影响因子：
--
作者：
Zhicai Zhang;F. Richard Yu;Fang Fu;Qiao Yan;Zhouyang Wang
通讯作者：
Zhicai Zhang;F. Richard Yu;Fang Fu;Qiao Yan;Zhouyang Wang
影响因子：
8.3
作者：
Han, Di;Chen, Wei;Fang, Yuguang
通讯作者：
Fang, Yuguang