Detection Algorithm Of Orthogonal Frequency Division Multiplexing With Index Modulation

As a promising technology,index modulation has been widely studied and applied in wireless communication.Because the orthogonal frequency division multiplexing with index modulation(OFDM-IM)system does not use all the active subcarrier patterns(SAPs)at the transmitter,that is,the existence of some invalid SAPs at the transmitter causes the low BER performance of OFDM-IM system.Because of the special index modulation mechanism of OFDM-IM system,the complexity of the receiver maximum likelihood(ML)algorithm is very high.Although the sub-optimal detector reduces the complexity compared with the ML detector to some extent,it still relies on the perfect channel state information(CSI).In the actual communication scenario,the perfect CSI needs high system overhead.This paper mainly studies the above problems from three directions,namely,improving the error performance and reducing the complexity of the detector,and minimizing the dependence on perfect CSI.Firstly,aiming at the problem that the complexity of ML detection algorithm in OFDM-IM system increases exponentially with the modulation order and the number of active subcarriers in the subgroup,a new greedy hard limit(GD-HL)algorithm with lower complexity is proposed.The system performance and computational complexity of three detection algorithms,ML algorithm,GD algorithm and GD-HL algorithm are compared and analyzed.The simulation results show that compared with the traditional ML algorithm,the complexity of GD-HL algorithm is greatly reduced in the same modulation mode,and the complexity of GD-HL algorithm does not increase with the modulation order M of OFDM-IM system.It is also verified that GD-HL algorithm is not affected by the uncertain CSI.Then,to solve the problem that some of the SAPs are not used in the OFDM-IM transmitter,an OFDM-IM system with all active subcarrier patterns used(ASAP-OFDM-IM)is proposed.ASAP-OFDM-IM uses all the SAPs by designing the mapping of the active subcarrier patterns in the OFDM-IM transmitter.Simulation and theoretical analysis show that the performance of ASAP-OFDM-IM is about 2d B better than that of OFDM-IM and 1d B better than that of dual-mode OFDM-IM(DM-OFDM)system when the spectrum efficiency is 2.22bits/s/Hz and the BER is 10^-5.Finally,for the problem that the traditional detection algorithm needs to be based on perfect CSI,for convenience and practicality,it is considered to be able to perform detection on the receiver with uncertain CSI.The detection algorithm of Least Square with Denoising Autoencoder(LSDAE)for OFDM-IM system is proposed by combining non-iterative LS algorithm with denoising autoencoder(DAE)in the field of deep learning.We first train the LSDAE network model,then optimize the parameters,and finally save the model for online testing.In the case of perfect CSI and imperfect CSI,the BER performance and complexity of LSDAE detector and ML detector are compared.Simulation results show that the LSDAE detector achieves good BER performance and reduces the dependence of perfect CSI.