Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Performance Analysis of Wireless Communication Systems Using Deep Learning Based Transmit Power Control in Nakagami Fading Channels

나카가미 페이딩 채널에서 딥러닝 기반 송신 전력 제어 기법을 이용하는 무선통신 시스템에 대한 성능 분석

  • Kim, Donghyeon (School of Electronic and Electrical Engineering, Hankyong National University) ;
  • Kim, Dongyon (School of Electronic and Electrical Engineering, Hankyong National University) ;
  • Lee, In-Ho (School of Electronic and Electrical Engineering, Hankyong National University)
  • Received : 2020.03.04
  • Accepted : 2020.03.31
  • Published : 2020.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a deep learning based transmit power control (TPC) scheme to improve the spectral and energy efficiency of wireless communication systems. In the wireless communication system, the positions of multiple transceivers follow a uniform distribution, and the performances of spectral and energy efficiency for the proposed TPC scheme are analyzed assuming the Nakagami fading channels. The proposed TPC scheme uses batch normalization to improve spectral and energy efficiency in deep learning based training. Through simulation, we compare the results of the spectral and energy efficiency of the proposed TPC scheme and the conventional one for various area sizes that limit the position range of the transceivers and Nakagami fading factors. Comparing the performance results, we verify that the proposed scheme provides better performance than the conventional one.

본 논문에서는 무선통신 시스템의 주파수 효율과 에너지 효율을 개선하기 위하여 딥러닝 기반의 송신 전력 제어 기법을 제안한다. 무선통신 시스템에서 다수의 송수신기의 위치는 균일 분포를 따르고 송수신기 간 채널은 나카가미 페이딩 채널을 가정하여 제안하는 송신 전력 제어 기법에 대한 주파수 효율과 에너지 효율의 성능을 분석한다. 제안하는 송신 전력 제어 기법은 딥러닝 기반의 학습에서 주파수 효율과 에너지 효율을 개선하기 위하여 배치 정규화 기법을 이용한다. 시뮬레이션을 통해 송수신기의 위치 범위를 제한하는 지형적 크기와 나카가미 페이딩 지수에 대하여 제안하는 송신 전력 제어 기법과 기존의 송신 전력 제어 기법의 주파수 효율과 에너지 효율의 성능 결과를 비교한다. 성능 결과의 비교를 통해 제안하는 기법이 기존의 기법보다 우수한 성능을 제공함을 입증한다.

Keywords

References

  1. W. Lee, T.-W. Ban, S. H. Kim, and J. Ryu, "Neighbor discovery for mobile systems based on deep learning," Journal of the Korea Institute of Information and Communication Engineering, vol. 12, no. 3, pp. 527-533, Mar. 2018.
  2. M. Kim, W. Lee, and D. Cho, "A novel PAPR reduction scheme for OFDM system based on deep learning," IEEE Communications Letters, vol. 22, no. 3, pp. 510-513, Mar. 2018. https://doi.org/10.1109/LCOMM.2017.2787646
  3. M. Kim, N. Kim, W. Lee, and D. Cho, "Deep learning aided SCMA," IEEE Communications Letters, vol. 22, no. 4, pp.720-723, Apr. 2018. https://doi.org/10.1109/LCOMM.2018.2792019
  4. H. Sun, X. Chen, Q. Shi, M. Hong, X. Fu, and N. D. Sidiropoulos, "Learning to optimize: training deep neural networks for interference management," IEEE Transactions on Signal Processing, vol. 66, no. 20, pp. 5438-5453, Oct. 2018. https://doi.org/10.1109/TSP.2018.2866382
  5. Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, "An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel," IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4331-4340, Sep. 2011. https://doi.org/10.1109/TSP.2011.2147784
  6. W. Lee, M. Kim, and D. Cho, "Deep power control: transmit power control scheme based on convolutional neural network," IEEE Communications Letters, vol. 22, no. 6, pp. 1276-1279, Jun. 2018. https://doi.org/10.1109/LCOMM.2018.2825444
  7. S. Ioffe, and C. Szegedy, "Batch normalization: accelerating deep network training by reducing internal covariate shift," Proceedings of the 32nd International Conference on Machine Learning, Lille: France, pp. 448-456, 2015.
  8. K. Simonyan, and A. Zisserman. (2014). "Very deep convolutional networks for large-scale image recognition," [Internet]. Available: https://arxiv.org/abs/1409.1556.
  9. Kingma, Diederik P., and Jimmy Ba. (2014) "Adam: A method for stochastic optimization," [Internet]. Available: https://arxiv.org/abs/1412.6980.
  10. IEEE 802.20 Channel Models Document, IEEE Standard 802.20-PD-08r1, IEEE, Jan. 2007.