Deep Learning as Applied to Wireless Networks: A Review

11 Pages Posted: 30 Nov 2020

Date Written: November 24, 2020

Abstract

The rapid development in the domain of wireless communication and high traffic of mobile data led to introduce an advanced model called Deep Learning (DL). It has been proposed in the literature, which is a subset of Machine learning. It is a technology that is considered to be an evolution for mobile data traffic management. A large amount of heterogeneous data is generated by wireless systems which is difficult to compute so to control such data various optimization tools are discussed. DL makes wireless communication more intelligent and versatile. Here, a review of the DL platform and different techniques to enable DL in wireless communications is summarized. There are different domains in wireless networking like networking, routing, and scheduling in which DL is employed. All such domains are discussed in this paper. Further different optimization techniques and recent advancement in this area were discussed.

Keywords: Deep learning, Fog computing, Mobile network, Wireless network, Reinforcement learning

Suggested Citation

Kochhar, Shewangi and Garg, Roopali, Deep Learning as Applied to Wireless Networks: A Review (November 24, 2020). Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3736467 or http://dx.doi.org/10.2139/ssrn.3736467

Shewangi Kochhar (Contact Author)

Panjab University ( email )

Sector 14
Sector 14
Chandigarh, 160014
India

Roopali Garg

Panjab University ( email )

Sector 14
Sector 14
Chandigarh, 160014
India

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