TY - GEN
T1 - Supervised learning method for link adaptation algorithm in coded MIMO-OFDM systems
AU - Zhang, Wenshuo
AU - Zheng, Liming
AU - Xu, Yao
AU - Wang, Gang
AU - Wu, Yue
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12
Y1 - 2018/12
N2 - The traditional link adaptation scheme requires packet error rate based on channel state information (CSI). The adaptive selection of the modulation and coding schemes (MCS) is usually decided according to estimation of packet error rate. Unfortunately, the integration of channel coding, OFDM technology and multi-antenna technology in Multiple-Input, Multiple-Output-Orthogonal Frequency Division Multiplexing(MIMOOFDM) wireless communication systems makes traditional prediction of packet error rate very complicated. This paper proposes a new framework based on supervised learning approach k-nearest neighbor (k-NN) algorithm for adaptive modulation and coding (AMC) in MIMO-OFDM wireless systems. With the singular value decomposition (SVD) of the channel matrix, the signal-to-noise ratio (SNR) on each spatial stream is extracted as a feature set. A classification scheme is then proposed to match channel implementations to different MCSs. The simulation results show that the proposed framework can successfully classify each MCS and perform perfect selection of MCS for frequency flat fading channels.
AB - The traditional link adaptation scheme requires packet error rate based on channel state information (CSI). The adaptive selection of the modulation and coding schemes (MCS) is usually decided according to estimation of packet error rate. Unfortunately, the integration of channel coding, OFDM technology and multi-antenna technology in Multiple-Input, Multiple-Output-Orthogonal Frequency Division Multiplexing(MIMOOFDM) wireless communication systems makes traditional prediction of packet error rate very complicated. This paper proposes a new framework based on supervised learning approach k-nearest neighbor (k-NN) algorithm for adaptive modulation and coding (AMC) in MIMO-OFDM wireless systems. With the singular value decomposition (SVD) of the channel matrix, the signal-to-noise ratio (SNR) on each spatial stream is extracted as a feature set. A classification scheme is then proposed to match channel implementations to different MCSs. The simulation results show that the proposed framework can successfully classify each MCS and perform perfect selection of MCS for frequency flat fading channels.
KW - Adaptive modulation and coding
KW - K-nearest neighbor algorithm
KW - MIMO-OFDM
KW - Supervised learning
UR - https://www.scopus.com/pages/publications/85070837606
U2 - 10.1109/CompComm.2018.8780721
DO - 10.1109/CompComm.2018.8780721
M3 - 会议稿件
AN - SCOPUS:85070837606
T3 - 2018 IEEE 4th International Conference on Computer and Communications, ICCC 2018
SP - 414
EP - 419
BT - 2018 IEEE 4th International Conference on Computer and Communications, ICCC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Computer and Communications, ICCC 2018
Y2 - 7 December 2018 through 10 December 2018
ER -