Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks

  • Dan Huang
  • Yuan GaoEmail author
  • Yi Li
  • Mengshu Hou
  • Wanbin Tang
  • Shaochi Cheng
  • Xiangyang Li
  • Yunchuan Sun


Wireless personal communication has become popular with the rapid development of 5G communication systems. Critical demands on transmission speed and QoS make it difficult to upgrade current wireless personal communication systems. In this paper, we develop a novel resource allocation method using deep learning to squeeze the benefits of resource utilization. By generating the convolutional neural network using channel information, resource allocation is to be optimized. The deep learning method could help make full use of the small scale channel information instead of traditional resource optimization, especially when the channel environment is changing fast. Simulation results indicate the fact that the performance of our proposed method is close to MMSE method and better than ZF method, and the time consumption of computation is smaller than traditional method.


Deep learning Resource allocation Downlink 5G Wireless network 



This work is funded by National Nature Science Foundation of China under grant of 61701503. The author would also like to thank all the reviewers, their suggestions help improve my work a lot.

Compliance with ethical standards

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


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Authors and Affiliations

  1. 1.University of Electronic Science and Technology of China (UESTC)SichuanChina
  2. 2.Academy of Military Science of PLABeijingChina
  3. 3.State Key Laboratory on Microwave and Digital Communications, National Laboratory for Information Science and TechnologyTsinghua UniversityBeijingChina
  4. 4.The High School Affiliated to Renmin University of ChinaBeijingChina
  5. 5.Business SchoolBeijing Normal UniversityBeijingChina

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