Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

被引:0
作者
Upadhyay, Shrikant [1 ]
Juluru, Tarun Kumar [2 ]
Deshmukh, Pooja, V [3 ]
Pawar, Aarti Prasad [3 ]
Mane, Snehal Chandrakant [3 ]
Singh, Charanjeet [4 ]
Shrivastava, Anurag [5 ]
机构
[1] MLR Inst Technol, Dept Elect & Commun Engn, Hyderabad, India
[2] Kakatiya Inst Technol & Sci, Dept ECE, Warangal, Telangana, India
[3] Bharati Vidyapeeth, Coll Engn, Pune, India
[4] Deenbandhu Chhotu Ram Univ Sci & Technol, Elect & Commun Dept, Murthal Sonipat, Haryana, India
[5] Saveetha Univ, Saveetha Inst Med & Tech Sci, Chennai 602105, Tamilnadu, India
关键词
Machine Learning; Beamforming; Massive MIMO; 5G Networks; Wireless Communication;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The following research focuses on the use of machine learning-based beamforming algorithms to improve Massive Multiple Input Multiple Output (MIMO) systems in 5G networks. Four unique algorithms namely, the Deep Learning Beamforming Algorithm (DLBA), Reinforcement Learning-Based Doa Estimation Algorithm (RLBEA), Clustering based beam forming algorithm(CBA) and GeneticAlgorithm Based Beam Forming Algoeithm were developed after which each of them was undertook evaluation. Widespread trials, in a simulated 5G environment, have revealed that the DLBA and RLBA considerably outperform other technologies by means of system throughput SINR as well Both the DLBA and RLBA achieved high system throughput, increased SINR levels and low BER. CBA and GABA, using clustering and genetic algorithms as their approaches, displayed moderate values on all assessed composite measures. This research offers important insights on the adaptability and learning potential of machine-learning based beamforming algorithms highlighting their ability to improve efficiency in wireless communication networks during the 5G revolution.
引用
收藏
页码:971 / 979
页数:9
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