A Machine Learning Framework for Enhancing 5G mmWave Radio Frequency Prediction

IF 0.9 Q4 TELECOMMUNICATIONS
Shantha Mary Joshitta, Dukhbhanjan Singh, Sagar Gulati, Pooja Sapra, Romil Jain, Diksha Aggarwal
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引用次数: 0

Abstract

5G mmWave technology offers high data rates and bandwidth, but high path loss and environmental changes affect signal quality. Existing models are not suitable for mmWave channels due to their varying nature over time. To overcome these challenges, this research presents an efficient time-dependent channel modeling framework based on a cuttlefish search-inspired efficient support vector machine (CS-ESVM) for predicting channel characteristics in large-scale measurement and RF at specific measurements at LOS and NLOS. The model is proposed to work for measurements at 28 GHz at a substation. The model also combines a prediction model and playback model for accurate channel characteristics key metrics such as root mean square error (RMSE), mean absolute percent error (MAPE), and correlation coefficient (CC), predicting the radio frequency. The proposed CS-ESVM model achieved the lowest RMSE values of 2.510 (LOS corridor), 1.210 (LOS hall), 1.815 (NLOS corridor), and 1.917 (NLOS hall), the lowest MAPE values of 0.009, 0.004, 0.003, and 0.007, and the highest CC values of 0.899, 0.969, 0.921, and 0.985. The findings suggest that CS-ESVM is more effective at predicting the mmWave channel's characteristics than traditional approaches. In conclusion, this ML-based framework improves the projection of 5G mmWave RF channels and provides a stable solution for real-time prediction in future network environments.

增强5G毫米波射频预测的机器学习框架
5G毫米波技术提供高数据速率和带宽,但高路径损耗和环境变化会影响信号质量。现有的模型不适合毫米波信道,因为它们随时间变化的性质。为了克服这些挑战,本研究提出了一种基于墨鱼搜索启发的高效支持向量机(CS-ESVM)的有效时相关信道建模框架,用于预测LOS和NLOS的大规模测量和RF特定测量中的信道特性。该模型适用于变电站28ghz频率的测量。该模型还结合了预测模型和重放模型,用于准确的信道特性关键指标,如均方根误差(RMSE)、平均绝对百分比误差(MAPE)和相关系数(CC),以预测射频。CS-ESVM模型的RMSE最小值为2.510 (LOS corridor)、1.210 (LOS hall)、1.815 (NLOS corridor)和1.917 (NLOS hall), MAPE最小值为0.009、0.004、0.003和0.007,CC最高值为0.899、0.969、0.921和0.985。研究结果表明,CS-ESVM在预测毫米波信道特性方面比传统方法更有效。综上所述,该基于ml的框架改进了5G毫米波射频信道的投影,为未来网络环境下的实时预测提供了稳定的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
3.10
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0.00%
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