DEEP LEARNING AIDED PARAMETRIC CHANNEL COVARIANCE MATRIX ESTIMATION FOR MILLIMETER WAVE HYBRID MASSIVE MIMO

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2021-9-09

Author

Esen, Özbay

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Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher than those being used in previous wireless communications systems, are utilized to meet the increased throughput requirements that come with 5G communications. The high levels of attenuation experienced by electromagnetic waves in these frequencies causes MIMO channels to have high spatial correlation. To attain desirable error performances, systems require knowledge about the channel correlations. In this thesis, a deep neural network aided method is proposed for the parametric estimation of the channel covariance matrix (CCM), which contains information regarding the channel correlations. When compared to some methods found in the literature, the proposed method yields satisfactory peformance in terms of both computational complexity and channel estimation errors.

Subject Keywords

5G, MIMO, mmWave, CCM, Deep Learning, Beamforming

URI

https://hdl.handle.net/11511/93197

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

Ö. Esen, “DEEP LEARNING AIDED PARAMETRIC CHANNEL COVARIANCE MATRIX ESTIMATION FOR MILLIMETER WAVE HYBRID MASSIVE MIMO,” M.S. - Master of Science, Middle East Technical University, 2021.