Theoretical performance analysis assisted by machine learning for spatial permutation modulation (SPM) in slow-fading channels

Jhih Wei Shih, Jung Chun Chi, Yuan Hao Huang, Pei Yun Tsai, I. Wei Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Based on spatial modulation (SM), spatial permu- tation modulation (SPM) has been recently proposed to enhance the performance of the multiple-input multiple-output (MIMO) system. SPM maps data bits to both the QAM symbol and permutation array. At successive time instants, different transmit antennas are activated according to the mapped permutation array to transmit the QAM symbol. In this work, the error rate of SPM in slow-fading channels is analyzed. The performance is first analyzed with the closed-form expression for the special case, and then is generalized to arbitrary cases by using the approximation of Gamma random variables. The machine learning algorithm is adopted to simplify the generalization and estimate the diversity. Through the analyses, we discover that by simply adding transmit antennas, the performance of SPM in slow-fading channels can be greatly enhanced due to the reduction of the time dependency. Numerical simulations demonstrate the accuracy of our analyses and show that by adding one transmit antenna, the time dependency can almost be removed, leading to around 3 dB SNR gain for the BER performance.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781538631805
DOIs
Publication statusPublished - 2018 Jul 27
Event2018 IEEE International Conference on Communications, ICC 2018 - Kansas City, United States
Duration: 2018 May 202018 May 24

Publication series

NameIEEE International Conference on Communications
Volume2018-May
ISSN (Print)1550-3607

Conference

Conference2018 IEEE International Conference on Communications, ICC 2018
CountryUnited States
CityKansas City
Period18/5/2018/5/24

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Keywords

  • Error rate analysis
  • Machine learning
  • Multiple-input multiple-output (MIMO)
  • Slow-fading channel
  • Spatial modulation (SM)
  • Spatial permutation modulation (SPM)

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Cite this

Shih, J. W., Chi, J. C., Huang, Y. H., Tsai, P. Y., & Lai, I. W. (2018). Theoretical performance analysis assisted by machine learning for spatial permutation modulation (SPM) in slow-fading channels. In 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings [8422471] (IEEE International Conference on Communications; Vol. 2018-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICC.2018.8422471