面向 5G 和 LTE 频谱传感通信的深度学习策略

Suham A. Albderi
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引用次数: 0

摘要

5G 创新理念是提高数据传输和收集速度以及各地许可可及性的普遍手段。尽管如此,第五个时代的融合将为数据传输过程提供一个敏锐的程序。在 5G 网络中,发送和获取信号的工作高度协调,因为这种创新安排了灵活的地球静止通信与其他中、小电路通信,并在直线通信中进行短距离转向,通信包括信号处理和寻路。在本研究中,将通过应用混合深度学习方法来测试通信范围的响应性改进,其中数据交叉将随着智能控制的升级而减少。利用混合深度学习方法,本研究展示了 5G 传输在敏锐检测 5G 远程传感器网络中的 LTE 信号范围和不同数据方面带来的巨大困难。道路障碍被认为是主要障碍。在规划第五代网络和传感器时,应考虑对应框架的状态。
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Deep Learning Strategies for 5G and LTE Spectrum Sensing Communication
The idea of 5G innovations is a prevalent instrument for the pace of transmission and gathering of data and the accessibility of permitting all over the place. Notwithstanding that the fifth era convergences will embrace a keen procedure for the data transmission process. Sending and getting signals work in high coordination in 5G networks, since this innovation arranges flexible, geostationary earthbound correspondence with other medium and little circuit correspondences with short steering in straight correspondences, and the correspondence incorporates signal processing as well as way finding. In this study the responsiveness improvement of the correspondence range will be tested by applying blended deep learning methods, in which the data cross-over will be diminished with the upgraded smart control. Utilizing blended deep learning methods, this study exhibits the huge difficulties presented by 5G transmissions in keenly detecting the LTE signal range and different data in 5G remote sensor networks. Way obstructions are recognized as the essential hindrance. The states of the correspondence framework ought to be considered while plotting the network and sensors for the fifth era.
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