Journal Article

Deep Learning Based Cooperative Resource Allocation in 5G Wireless Networks

Gao, Yuan, Sun, Yunchuan, Tang, Wanbin, Cheng, Shaochi, Huang, Dan, Hou, Mengshu, Li, Yi, Li, Xiangyang

2022

Overview

Wireless personal communication has become popular with the rapid development of 5G communication systems. Critical demands on transmission speed and QoS make it difficult to upgrade current wireless personal communication systems. In this paper, we develop a novel resource allocation method using deep learning to squeeze the benefits of resource utilization. By generating the convolutional neural network using channel information, resource allocation is to be optimized. The deep learning method could help make full use of the small scale channel information instead of traditional resource optimization, especially when the channel environment is changing fast. Simulation results indicate the fact that the performance of our proposed method is close to MMSE method and better than ZF method, and the time consumption of computation is smaller than traditional method.

• Publisher: Springer Nature B.V
• Subject: 5G mobile communication / Antennas / Artificial neural networks / Bandwidths / Communication / Communications systems / Computer simulation / Cooperation / Deep learning / Laboratories / Optimization / Personal communication / Resource allocation / Resource utilization / Simulation / Spectrum allocation / Teaching methods / Wireless communications / Wireless networks