Channel Estimation Using RIDNet Assisted OMP for Hybrid-Field THz Massive MIMO Systems


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Nayir H., Karakoca E., Görçin A., Qaraqe K.

2023 IEEE International Conference on Communications, ICC 2023, Rome, Italy, 28 May - 01 June 2023, vol.2023-May, pp.2625-2630 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2023-May
  • Doi Number: 10.1109/icc45041.2023.10279560
  • City: Rome
  • Country: Italy
  • Page Numbers: pp.2625-2630
  • Keywords: hybrid-field channel, massive MIMO, RIDNet, spectral efficiency, terahertz
  • Istanbul Technical University Affiliated: Yes

Abstract

The terahertz (THz) band radio access with larger available bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modeling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a real image denoising network (RIDNet) and orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including far-field and near-field constituents. The simulation experiments are performed using the ray-tracing tool. The results demonstrate that the proposed RIDNet-based method consistently provides lower channel estimation errors than the conventional OMP algorithm for all signal-to-noise ratio (SNR) regions. The performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.