Index modulation recognition (IMR) at secondary user (SU) receiver is a challenging topic for MIMO-OFDM cognitive radio network (MIMO-OFDM-CRN) with index modulation scheme, in order to make SU preferably adapt to the communication environment by adjusting own parameters. For index modulated signals, this paper proposes an effective IMR algorithm based on projection residual analysis (PRA). The proposed algorithm is suitable for various types of modulation such as spatial index modulation (SIM), frequency index modulation (FIM) and space-frequency index modulation (SFIM). Firstly the sparse structure of primary user (PU) signal is detected through removing the joint sparsity of signal matrix. Secondly, according to the detected sparse structure, the problem of whether the signal is index modulation (IM) or unindexed modulation (UIM) is addressed by projection residual analysis with ${z}$ -test. The hypothesis test judges whether the projection residual power of the received signal is significant compared with that of the UIM case, where the projection residual is obtained through projecting the subcarrier signals in the current index modulation symbol into the subspace of those in the previous symbol. The distribution of the test statistic is derived theoretically under UIM case. Thirdly, combining the detected sparse structure and the results of ${z}$ -test, the index modulation mode of PU signal is identified. Simulation results verify the performance of the proposed algorithm in terms of bit error rate (BER) and recognition rate, respectively.

中文翻译：

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基于投影残差分析的指数调制识别

次用户（SU）接收器的索引调制识别（IMR）是具有索引调制方案的MIMO-OFDM认知无线电网络（MIMO-OFDM-CRN）的一个具有挑战性的课题，以使SU通过调整自身来更好地适应通信环境参数。针对索引调制信号，本文提出了一种基于投影残差分析（PRA）的有效IMR算法。该算法适用于各种调制类型，如空间索引调制（SIM）、频率索引调制（FIM）和空频索引调制（SFIM）。首先通过去除信号矩阵的联合稀疏性来检测主用户（PU）信号的稀疏结构。其次，根据检测到的稀疏结构， ${z}$ -测试。假设检验判断接收信号的投影残差功率与UIM情况相比是否显着，UIM情况是通过将当前索引调制符号中的子载波信号投影到前一个符号的子空间中得到投影残差。检验统计量的分布是在 UIM 情况下理论上推导出来的。第三，结合检测到的稀疏结构和结果 ${z}$ -test，识别PU信号的指数调制方式。仿真结果分别验证了所提算法在误码率（BER）和识别率方面的性能。