Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks

被引:185
作者
Liang, Le [1 ]
Ye, Hao [2 ]
Yu, Guanding [3 ]
Li, Geoffrey Ye [2 ]
机构
[1] Intel Labs, Hillsboro, OR 97124 USA
[2] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA
[3] Zhejiang Univ, Dept Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China
基金
美国国家科学基金会;
关键词
Resource management; Wireless communication; Optimization; Deep learning; Mathematical model; Computational modeling; Interference; reinforcement learning (RL); resource allocation; vehicular networks; wireless communications; CROSS-LAYER OPTIMIZATION; NEURAL-NETWORKS; PART I; POWER; OFDM; OPTIMALITY; MANAGEMENT; INTELLIGENCE; SUBCARRIER; SYSTEMS;
D O I
10.1109/JPROC.2019.2957798
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
It has been a long-held belief that judicious resource allocation is critical to mitigating interference, improving network efficiency, and ultimately optimizing wireless communication performance. The traditional wisdom is to explicitly formulate resource allocation as an optimization problem and then exploit mathematical programming to solve the problem to a certain level of optimality. Nonetheless, as wireless networks become increasingly diverse and complex, for example, in the high-mobility vehicular networks, the current design methodologies face significant challenges and thus call for rethinking of the traditional design philosophy. Meanwhile, deep learning, with many success stories in various disciplines, represents a promising alternative due to its remarkable power to leverage data for problem solving. In this article, we discuss the key motivations and roadblocks of using deep learning for wireless resource allocation with application to vehicular networks. We review major recent studies that mobilize the deep-learning philosophy in wireless resource allocation and achieve impressive results. We first discuss deep-learning-assisted optimization for resource allocation. We then highlight the deep reinforcement learning approach to address resource allocation problems that are difficult to handle in the traditional optimization framework. We also identify some research directions that deserve further investigation.
引用
收藏
页码:341 / 356
页数:16
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