基于深度学习的5G接入技术优化研究

Research on Optimization of 5G Wireless Access Technology Based on Deep Learning

5G接入技术是实现高速率、低延时、大连接的关键。然而,5G网络复杂的架构和海量的数据对接入技术提出了巨大挑战。该文针对5G接入网中的信道估计、波束赋形、资源调度等关键技术,提出了基于深度学习的优化方法。首先,采用卷积神经网络实现信道估计,有效提高估计精度;其次,利用深度强化学习实现波束赋形,提升波束准确性;最后,构建基于深度神经网络的资源调度模型,改善系统吞吐量。仿真结果表明,所提出的优化方法能够显著增强5G接入网性能,为5G网络的部署和优化提供了新的思路。

5G wireless access technology is the key to achieving high speed,low latency,and large connectivity.However,the complex architecture and massive data of 5G networks pose significant challenges to wireless access technology.The article proposes optimization methods based on deep learning for key technologies such as channel estimation,beamforming,and resource scheduling in 5G wireless access.Firstly,using convolutional neural networks to achieve channel estimation effectively improves estimation accuracy;Secondly,utilizing deep reinforcement learning to achieve beamforming and improve beam accuracy;Finally,a resource scheduling model based on deep neural networks is constructed to improve system throughput.The simulation results show that the proposed optimization method can significantly enhance the performance of 5G wireless access,providing new ideas for the deployment and optimization of 5G networks.