UVicSpace

Green Wireless Transmissions for Advanced Internet of Things: Session-Specific Analysis and Design with Deep Reinforcement Learning

Show simple item record

dc.contributor.author Xu, Fang
dc.date.accessioned 2022-07-08T23:22:41Z
dc.date.available 2022-07-08T23:22:41Z
dc.date.copyright 2022 en_US
dc.date.issued 2022-07-08
dc.identifier.uri http://hdl.handle.net/1828/14054
dc.description.abstract Reliable and energy-efficient wireless data transmission is of great importance to the success of future Internet of Things (IoT). In this thesis, we analyze and minimize the energy consumption of wireless data transmissions from an individual transmission perspective. In particular, we investigate three fundamental transmission scenarios, namely point-to-point transmission, data collection from wirelessly-powered sensor, and cooperative relaying transmission with wireless power transfer. For point-to-point transmission, we analyze and optimize ideal continuous rate adaptation and continuous power adaptation transmission schemes, where the closed-form expressions for all optimal parameters are derived. These results establish an energy consumption lower limit for wireless data transmissions over fading channels. In the case of data collection from wirelessly-powered sensor scenario, we derive closed-form expressions for optimal transmission parameters for ideal rate adaptive transmission with linear energy harvesting setting. Under more practical assumptions of finite block-length transmission with nonlinear energy harvesting, we propose a deep reinforcement learning (DRL) based approach to arrive at a deep policy network for determining the near-optimal transmission parameters in real time. An online tuning method is also proposed to adjust the policy network using online experience to cater for model inaccuracy and environment variation. Finally, for the case of cooperative relaying transmission with wireless power transfer, under ideal rate adaptive transmission with piecewise linear energy harvesting, we derive the closed-form expressions for all optimal transmission parameters. Then, the optimal design problem is again generalized to the finite block-length transmission with nonlinear energy harvesting setting, where we again apply the DRL-based method to train and tune a deep policy network for determining the near-optimal transmission parameters in real time. For all cases, we illustrate various design tradeoffs through selected numerical examples. Besides improving the energy efficiency of wireless transmissions for future IoT applications, our proposed DRL-based method can serve as a general solution for real-time optimal design problems in wireless communications. en_US
dc.language English eng
dc.language.iso en en_US
dc.rights Available to the World Wide Web en_US
dc.title Green Wireless Transmissions for Advanced Internet of Things: Session-Specific Analysis and Design with Deep Reinforcement Learning en_US
dc.type Thesis en_US
dc.contributor.supervisor Yang, Hong-Chuan
dc.degree.department Department of Electrical and Computer Engineering en_US
dc.degree.level Doctor of Philosophy Ph.D. en_US
dc.description.scholarlevel Graduate en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search UVicSpace


Browse

My Account

Statistics

Help