Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks
- 21 Downloads
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.
KeywordsDeep 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.
- 1.3GPP, Website Available Online: http://www.3gpp.org/release-16
- 3.Benchaabene Y, Boujnah N, Zarai F (2017) 5G Cellular: Survey on Some Challenging Techniques, 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Taipei, pp. 348-353Google Scholar
- 14.Ferreira PVR et al (2018) Multi-objective Reinforcement Learning for Cognitive Satellite Communications using Deep Neural Network Ensembles, in IEEE Journal on Selected Areas in CommunicationsGoogle Scholar
- 15.Atallah R, Assi C, Khabbaz M (2017) Deep reinforcement learning-based scheduling for roadside communication networks, 2017 15th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), Paris, pp. 1-8Google Scholar
- 16.Dorner S, Cammerer S, Hoydis J, ten Brink S (2017) On deep learning-based communication over the air, 2017 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, pp. 1791-1795Google Scholar
- 17.Challita U, Dong L, Saad W (2017) Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective, in IEEE Transactions on Wireless CommunicationsGoogle Scholar
- 18.Challita U, Dong L, Saad W (2017) Deep Learning for Proactive Resource Allocation in LTE-U Networks, European Wireless 2017; 23th European Wireless Conference, Dresden, Germany, pp. 1-6Google Scholar
- 19.Lee M, Xiong Y, Yu G, Li GY (2018) Deep Neural Networks for Linear Sum Assignment Problems," in IEEE Wireless Communications LettersGoogle Scholar
- 20.Li H, Wei T, Ren A, Zhu Q, Wang Y (2017) Deep reinforcement learning: Framework, applications, and embedded implementations: Invited paper, 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), Irvine, pp. 847-854Google Scholar
- 21.Hackett TM, Bilén SG, Ferreira PVR, Wyglinski AM, Reinhart RC (2017) Implementation of a space communications cognitive engine, 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA), Cleveland, pp. 1-7Google Scholar
- 22.AlQerm I, Shihada B (2017) Enhanced machine learning scheme for energy efficient resource allocation in 5G heterogeneous cloud radio access networks, 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, pp. 1-7Google Scholar
- 23.Chen M, Saad W, Yin C (2017) Liquid State Machine Learning for Resource Allocation in a Network of Cache-Enabled LTE-U UAVs," GLOBECOM 2017 - 2017 IEEE Global Communications Conference, Singapore, pp. 1-6Google Scholar