Deep Neural Network-based Power Control for Green Wireless Communications

• Main Authors: Ting-Jui Lin, 林庭瑞
• Other Authors: Meng-Lin Ku
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/c96ydu

碩士 === 國立中央大學 === 通訊工程學系 === 107 === In recent years, the artificial intelligence and the Internet of Things have been widely developed in wireless communication applications. The increasing demand on energy consumption in wireless communications has stimulated the rapid development of energy harvesting technology to solve the problem of limited battery power at wireless nodes. The convex optimization is a common approach to control transmission power over a future finite time horizon for achieving the maximum system throughput. It, however, requires non-causal perfect knowledge of energy harvesting patterns and channel gains over the power control duration due to the battery storage and the energy causality constraints, resulting in high complexity and impractical in real applications. To overcome this problem, in this thesis, deep neural networks (DNNs), along with the use of past energy harvesting patterns, are proposed to predict the future power control values, which can effectively reduce the computation complexity for real-time applications. Specifically, the DNN-based power control schemes are proposed for maximizing the system throughput in two scenarios of energy harvesting communications: single-user and multi-user setups. In the single-user scenario, the convex optimization is utilized to generate the optimal power control values based on the historic energy harvesting data, and the results are later used in the multilayer perceptron (MLP) to learn its input/output relationships for power control prediction. In the multi-user scenario, centralized and distributed MLP-based power control schemes are investigated, and the main difference between them lies that the former scheme requires the past energy harvesting conditions and channel gains of all users, while each user only requires the past energy harvesting conditions, channel gains and interference power values related to itself in the later scheme. For the learning of MLPs in the multi-user scenario, a weighted-sum minimum mean-square-error (MMSE) approach and an iterative directional water-filling approach are respectively proposed to generate the reference power control solutions in the centralized and distributed designs. The simulation results show that the proposed method can greatly reduce the computational complexity and approach the system throughput of the reference solutions very well in both single-user and multi-user application.