Performance of Multi-antenna Features and Configurations

Asplund, Henrik; Astély, David; Butovitsch, Peter von; Chapman, Thomas; Frenne, Mattias; Ghasemzadeh, Farshid; Hagström, Måns; Hogan, Billy; Jöngren, George; Karlsson, Jonas; Kronestedt, Fredric; Larsson, Elisabeth

doi:10.1016/b978-0-12-820046-9.00013-7

Cited by 2 publications

(1 citation statement)

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“…Large-scale antennas can also be deployed in ultra-dense cells and hotspots to effectively reduce path loss, delay, and interference to achieve a reduction in power consumption [5]. Some studies [6][7][8] have investigated large-scale antenna techniques that simulate stationary beamforming and channel state precoding. Two studies [9,10] focused on the capacity of downlink beamforming based on multiple-input and multiple-output (MIMO) and gave a closed expression.…”

Section: Introductionmentioning

confidence: 99%

Technique for Large-Scale Antenna Beamforming Based on Neural Network

Xie

Guo

JianZhou

2022

Wireless Communications and Mobile Computing

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The commercialization of the fifth-generation technology and the rapid development of Internet of Things technology have made mobile communication networks increasingly complex. Simultaneously, the massive terminal connections have caused serious interference between networks. Large-scale array antennas have become a hot topic of recent studies to improve the wireless transmission characteristics and spectrum resource utilization efficiency. This study was aimed at exploring using large-scale array antennas combined with neural networks in ultradense cells to improve the wireless signal transmission quality. The simulation results showed that the scheme realized not only the wireless signal transmission with a neural network but also the effective recovery of the source signal. Similarly, the application of this scheme effectively reduced the power consumption during signal transmission, the delay, and the interference.

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