Predicting the Path Loss of Wireless Channel Models Using Machine Learning Techniques in MmWave Urban Communications

• URL: http://arxiv.org/abs/2005.00745v1
• Date: Sat, 2 May 2020 08:19:18 GMT
• Title: Predicting the Path Loss of Wireless Channel Models Using Machine Learning Techniques in MmWave Urban Communications
• Authors: Saud Aldossari, Kwang-Cheng Chen
• Abstract summary: Classic wireless communication channel modeling is performed using Deterministic and channel methodologies. Machine learning (ML) emerges to revolutionize system design for 5G and beyond.
• Score: 13.026091318474785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The classic wireless communication channel modeling is performed using Deterministic and Stochastic channel methodologies. Machine learning (ML) emerges to revolutionize system design for 5G and beyond. ML techniques such as supervise leaning methods will be used to predict the wireless channel path loss of a variate of environments base on a certain dataset. The propagation signal of communication systems fundamentals is focusing on channel modeling particularly for new frequency bands such as MmWave. Machine learning can facilitate rapid channel modeling for 5G and beyond wireless communication systems due to the availability of partially relevant channel measurement data and model. When irregularity of the wireless channels lead to a complex methodology to achieve accurate models, appropriate machine learning methodology explores to reduce the complexity and increase the accuracy. In this paper, we demonstrate alternative procedures beyond traditional channel modeling to enhance the path loss models using machine learning techniques, to alleviate the dilemma of channel complexity and time-consuming process that the measurements were taken. This demonstrated regression uses the measurement data of a certain scenario to successfully assist the prediction of path loss model of a different operating environment.

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