Waterfall Model for Deep Reinforcement Learning Based Scheduling
| Autor: | Zheng-Wei Liu, 劉政威 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 The fourth generation of communication systems has been able to meet the multimedia application needs of mobile devices. Through the scheduling service provided by the base station, the user equipment can obtain the data packets required by the downlink of the communication system to meet and obtain better application services, so the channel resources are allocated and the calculation of the user group scheduling service is provided. The law is quite critical. This paper implements a mobile communication scheduling learning platform, and proposes a Deep Deterministic Policy Gradient model. The waterfall model concept is used to analyze the scheduling algorithm flow into three stages: sorting selection, resource evaluation and channel allocation. A waterfall scheduling method that enables more data throughput per unit time and meets more user needs in the current communication environment. The mobile communication scheduling learning platform is composed of six modular components: base station and channel resources, enhanced learning neural network, user equipment attributes, application service types, environmental information and reward functions, and phase micro-algorithms and dependency injection.. Using inversion control and dependency injection to reduce platform software coupling, it is quite easy to maintain the stage micro-algorithm and the six module components. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|