Modeling Millimeter Wave Channels with Generative Adversarial Networks

Authors

DOI:

https://doi.org/10.3103/S0735272724010035

Keywords:

millimeter wave, subterahertz, neural networks, channel modelling

Abstract

According to this research, modern cellular systems are becoming more and greater reliance on concurrent communication across several discontinuous bands as a result of wider bandwidth and macro-diversity. In millimetre wave (mmWave) and Terahertz (THz) frequencies, which are frequently paired with lower frequencies for resilience, multi-frequency communication is very crucial. Statistical models capable of representing the combined distribution of channel routes over many frequencies are needed to assess these systems. A broad neural network-based training approach for multi-frequency double-directional statistical channel models is presented in this research. The suggested method involves representing every channel as a multiclustered set and training a generative adversarial network (GAN) to generate random multi-cluster profiles. The resulting cluster data consists of vectors distributed at various frequencies that are random received powers, angle, and delay. Urban micro-cellular connections at 28 and 140 GHz are modelled using ray tracing data to demonstrate the methodology. The model is readily adaptable for multi-frequency link or network layer simulation. The model may capture intriguing statistical correlations between frequencies, as studies show, and the technique involves minimal statistical assumptions.

Author Biography

Yahia A. Zakaria, National Research Centre (NRC), Cairo

Systems and Information Department, Engineering Research Institute for New and Renewable Energy

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Published

2024-11-24

Issue

Section

Research Articles