Self-Interference Channel Training for Full-Duplex Massive MIMO Systems.

Taehyoung Kim, Kyungsik Min, Sangjoon Park
Author Information
  1. Taehyoung Kim: Department of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea. ORCID
  2. Kyungsik Min: Samsung Electronics Company Ltd., Suwon 16677, Korea.
  3. Sangjoon Park: Department of Electronic Engineering, Kyonggi University, Suwon 16227, Korea. ORCID

Abstract

Full-duplex (FD) is a promising technology for increasing the spectral efficiency of next-generation wireless communication systems. A major technical challenge in enabling FD in a real network is to remove the self-interference (SI) caused by simultaneous transmission and reception at the transceiver, and the SI cancellation performance depends significantly on the estimation accuracy of the SI channel. In this study, we proposed a novel partial SI channel training method for minimizing the residual SI power for FD massive multiple-input multiple-output (MIMO) systems. Based on an SI channel training framework under a limited training overhead, using the proposed scheme, the BS estimates only a part of the SI channel vectors, while skipping the channel training for the other remaining SI channel vectors by using their last estimates. With this partial training framework, the proposed scheme finds the optimal partial SI channel training strategy for pilot allocation to minimize the expected residual SI power, considering the time-varying Rician fading channel model for the SI channel. Therefore, the proposed scheme can improve the sum-rate performance compared with other simple partial training schemes for FD massive MIMO systems under a limited training overhead. Numerical results confirm the effectiveness of the proposed scheme for FD massive MIMO systems compared with the full training scheme, as well as other partial training schemes.

Keywords

References

  1. Sensors (Basel). 2016 Jul 21;16(7): [PMID: 27455256]
  2. Sensors (Basel). 2020 Jan 03;20(1): [PMID: 31947765]
  3. Sensors (Basel). 2020 Mar 17;20(6): [PMID: 32192163]
  4. Sensors (Basel). 2020 Nov 02;20(21): [PMID: 33147812]

Grants

  1. NRF-2016R1D1A1B03934546/National Research Foundation of Korea
  2. NRF-2019R1C1C1003202/National Research Foundation of Korea

Word Cloud

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