Modeling Millimeter Wave Channels with Generative Adversarial Networks
DOI:
https://doi.org/10.3103/S0735272724010035Keywords:
millimeter wave, subterahertz, neural networks, channel modellingAbstract
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.
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