TY - GEN
T1 - Theoretical performance analysis assisted by machine learning for spatial permutation modulation (SPM) in slow-fading channels
AU - Shih, Jhih Wei
AU - Chi, Jung Chun
AU - Huang, Yuan Hao
AU - Tsai, Pei Yun
AU - Lai, I. Wei
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/27
Y1 - 2018/7/27
N2 - 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.
AB - 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.
KW - Error rate analysis
KW - Machine learning
KW - Multiple-input multiple-output (MIMO)
KW - Slow-fading channel
KW - Spatial modulation (SM)
KW - Spatial permutation modulation (SPM)
UR - http://www.scopus.com/inward/record.url?scp=85051439132&partnerID=8YFLogxK
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U2 - 10.1109/ICC.2018.8422471
DO - 10.1109/ICC.2018.8422471
M3 - Conference contribution
AN - SCOPUS:85051439132
SN - 9781538631805
T3 - IEEE International Conference on Communications
BT - 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Communications, ICC 2018
Y2 - 20 May 2018 through 24 May 2018
ER -