Titlebook: Artificial Intelligence for Communications and Networks; First EAI Internatio Shuai Han,Liang Ye,Weixiao Meng Conference proceedings 2019 I

Iniquitous

https://doi.org/10.1007/978-3-663-14126-6, and the distributed algorithm can converge faster than the centralized algorithm. Moreover, both the proposed Q-learning algorithms outperform the random power control algorithm in terms of the system throughput and satisfaction ratio.

领先

https://doi.org/10.1007/978-3-663-14126-6ess of implantation, dissemination and application. Secondly, it discusses the existing problems and solutions in China. Finally, the application difficulties and coping strategies of information security technology are analyzed, which provides theoretical support for personal information security in the era of big data.

柏树

Grundlagen der Rolldynamik von Luftreifenxperiment results show that our method is more effective, less similarity of traffic compared with Multipath-Floyd method and single-path method, respectively. Moreover, it is difficult for attackers to capture the traffic feature and do not need key management, which reduces the cost of the controller.

Carcinogen

https://doi.org/10.1007/978-3-662-02128-6l result shows that the planning algorithm proposed in this paper can lower the network risk, station voltage level balance degree and the route similarity effectively, which provides a useful reference while planning service routing.

Infect

A Multi-agent Reinforcement Learning Based Power Control Algorithm for D2D Communication Underlaying, and the distributed algorithm can converge faster than the centralized algorithm. Moreover, both the proposed Q-learning algorithms outperform the random power control algorithm in terms of the system throughput and satisfaction ratio.

减弱不好

overrule

江湖郎中

Metastasis

自恋