论文信息 - Deep Learning for Resource Allocation of a Marine Vehicular Ad-Hoc Network

Deep Learning for Resource Allocation of a Marine Vehicular Ad-Hoc Network

The marine vehicular ad-hoc network (M-VANET) is one of prospective architectures to provide Internet services to various users in the oceanic environment. When multiple users share a single spectrum band, it highly desirable to design a optimal strategy to allocate the transmission power while mitigating the mutual interference. In this paper, we apply the deep learning (DL) methodology to solve such a problem.

Xian Liu

[1] A. Leon-Garcia,et al. Probability, statistics, and random processes for electrical engineering , 2008 .

[2] Wei Yu,et al. A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends , 2018, IEEE Access.

[3] Martin Haenggi,et al. Stochastic Geometry for Wireless Networks , 2012 .

[4] Zhi-Quan Luo,et al. Dynamic Spectrum Management: Complexity and Duality , 2008, IEEE Journal of Selected Topics in Signal Processing.

[5] Bernard Fino,et al. Multiuser detection: , 1999, Ann. des Télécommunications.

[6] Huosheng Hu,et al. Building Novel VHF-Based Wireless Sensor Networks for the Internet of Marine Things , 2018, IEEE Sensors Journal.

[7] Qi Hao,et al. Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey , 2018, IEEE Communications Surveys & Tutorials.

[8] Mohsen Guizani,et al. Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications , 2015, IEEE Communications Surveys & Tutorials.