A Deep-Tree-Model-Based Radio Resource Distribution for 5G Networks

Deep learning is a branch of machine learning that learns the high-level abstraction of data in a layered structure. Since its invention, it has been successfully applied in many image and speech processing applications. The success of deep learning depends on how big the data size is. Recently, the number of smart sensors and the Internet of Things have increased exponentially. This, in turn, has created huge traffic congestion in mobile and wireless communication networks. The available network resources need to be carefully utilized for seamless transmission of this large amount of data. Fortunately, deep learning performs very well with the big size of data. Therefore, the gap between machine learning research and advanced communication research should be narrowed down. In this article, we target an intelligent allocation of radio resources for 5G networks using deep learning. A framework consisting of a deep tree model and a long short-term memory network is proposed to predict future traffic congestion. Based on the prediction, the uplink and downlink ratio is adapted to utilize the resources optimally. Experimental results demonstrate that the proposed framework can achieve a low packet loss ratio and high throughput.

[1]  Stratis Ioannidis,et al.  Deep Learning Convolutional Neural Networks for Radio Identification , 2018, IEEE Communications Magazine.

[2]  Jie Yang,et al.  Data-Driven Deep Learning for Automatic Modulation Recognition in Cognitive Radios , 2019, IEEE Transactions on Vehicular Technology.

[3]  M. Shamim Hossain,et al.  Artificial-Intelligence-Based Data Analytics for Cognitive Communication in Heterogeneous Wireless Networks , 2019, IEEE Wireless Communications.

[4]  Nei Kato,et al.  A Deep-Learning-Based Radio Resource Assignment Technique for 5G Ultra Dense Networks , 2018, IEEE Network.

[5]  Andreas F. Molisch,et al.  Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach , 2018, IEEE Communications Magazine.

[6]  M. Shamim Hossain,et al.  A software defined network routing in wireless multihop network , 2017, J. Netw. Comput. Appl..

[7]  M. Shamim Hossain,et al.  Environment Classification for Urban Big Data Using Deep Learning , 2018, IEEE Communications Magazine.

[8]  Jeffrey H. Reed,et al.  Artificial Intelligence Defined 5G Radio Access Networks , 2018, IEEE Communications Magazine.

[9]  Muhammad Ghulam,et al.  Edge Computing with Cloud for Voice Disorder Assessment and Treatment , 2018, IEEE Communications Magazine.

[10]  Chih-Yu Wang,et al.  Distributed dynamic-TDD resource allocation in femtocell networks using evolutionary game , 2015, 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[11]  Bishal Thapa,et al.  Machine Learning Approach to RF Transmitter Identification , 2017, IEEE Journal of Radio Frequency Identification.

[12]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[13]  Ingrid Moerman,et al.  End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications , 2017, IEEE Access.

[14]  Muhammad Ghulam,et al.  Deep convolutional tree networks , 2019, Future Gener. Comput. Syst..

[15]  M. Shamim Hossain,et al.  Emotion recognition using deep learning approach from audio-visual emotional big data , 2019, Inf. Fusion.