###### Abstract
Based on the signal sparsity characteristics of the MIMO-OFDM-IM system, a low-complexity signal detector based on deep learning (DL) is proposed in [32], which firstly performs the feature information extraction by the acquired signals of the two subnetworks and the DL method, and then the optimized least squares method is used to demodulate the required communication information. It improves the bit error performance and reduces the computational complexity. Paper [33] demonstrated the effect of different signal detection methods on the transmission error bit rate of MIMO-OFDM-IM systems through the derivation of theoretical equations and puts forward its adaptability to visible light communication systems in another reference. In the proposed scheme, the spectral efficiency of the system is greatly improved due to the fact that the OFDM-IM waveforms emitted by each transmitting antenna do not interfere with each other. The method in [34] designs a novel MIMO-OFDM-IM waveform by deactivating a subset of subcarriers. Compared with the conventional MIMO-OFDM waveforms, the subcarrier design of this MIMO-OFDM-IM waveform is more flexible and can achieve a better BER performance, which leads to higher energy efficiency for practical systems. However, the detection of the MIMO-OFDM-IM communication signal becomes a very challenging problem due to the mutual interference between multiple channels and the correlation between different subcarriers of the same subblock [35; 36]. In addition, when MIMO-OFDM-IM is applied to radar, it is found that the traditional signal processing method has the problem of dynamic range decline [37; 38]. Therefore, under the integrated MIMO-OFDM-IM system, it is urgent to develop more waveform design schemes and corresponding signal processing schemes to improve the system performance. In this paper, MIMO-OFDM-IM technology is applied to the designed IRCS. After realizing the integrated signal design based on MIMO-OFDM-IM, an improved signal processing scheme is proposed. Firstly, according to the technical characteristics of MIMO-OFDM and IM, an integrated pulse signal design scheme based on MIMO-OFDM-IM is proposed. At the radar receiver, the communication information and phase difference information of the multipulse echo signal are compensated, and the sparse signal model is obtained by screening the effective data. In addition, a segmented coherent accumulation method based on sparse signal reconstruction is adopted to achieve target parameter estimation within the detection range. At the communication receiver, an improved communication signal detection method based on sequential Monte Carlo is explored. To this end, we summarize our contributions as follows:  
(1) The integrated signal design adopts the mode of multiple OFDM symbols in one pulse, which improves the communication transmission rate under the same bandwidth, and IM technology increases the subcarrier frequency spacing, thus having a better intercarrier interference (ICI) resistance capability. (2) In the integrated MIMO-OFDM-IM systems, an improved method based on information compensation and sparse signal reconstruction is proposed to estimate the range and velocity of radar targets. Compared with the existing methods, it has a better parameter estimation performance. (3) According to the subcarrier index, we introduce an improved communication signal detection method based on SMC in the proposed system. Compared with the existing methods, the improved method can achieve a lower communication error rate with the same spectral efficiency while ensuring a low computational complexity. The rest of this paper is organized as follows. In Section 2, we introduce the system model and signal design. In Section 3, the optimized radar and communication signal processing schemes are proposed. In Section 4, we use simulation results to verify the performance of the system. Finally, Section 5 summarizes the main work of this paper.