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- W3214389536 abstract "Terahertz communication is one of the most promising wireless communication technologies for 6G generation and beyond. For THz systems to be practically adopted, channel estimation is one of the key issues. We consider the problem of channel modeling and estimation with deterministic channel propagation and the related physical characteristics of THz bands, and benchmark various machine learning algorithms to estimate THz channel, including neural networks (NN), logistic regression (LR), and projected gradient ascent (PGA). Numerical results show that PGA algorithm yields the most promising performance at SNR=0 dB with NMSE of -12.8 dB." @default.
- W3214389536 created "2021-11-22" @default.
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- W3214389536 date "2022-05-23" @default.
- W3214389536 modified "2023-09-23" @default.
- W3214389536 title "Benchmarking Machine Learning Techniques for THz Channel Estimation Problems" @default.
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- W3214389536 doi "https://doi.org/10.23919/mipro55190.2022.9803741" @default.
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