一种面向RDSM系统的基于遗传算法优化色散矩阵的方法

矩形差分空间调制（rectangular differential spatial modulation，RDSM）是一种多天线非相干调制技术，该技术频谱效率高，低功耗且无信道估计开销，特别适用于信道快速变化的车联网、物联网、6G蜂窝网络等未来通信系统。然而发射端的稀疏酉矩形空时色散矩阵（dispersion matrix，DM）的构造问题一直是个难点，而当前使用的随机搜索优化方法具有极高的计算复杂度，对此，提出了一种低复杂度遗传算法（genetic algorithm， GA）。根据秩与行列式标准最大化准则（rank and determinant criterion，RDC）的方法计算适应度值，可避免差分系统中所需的分类讨论。根据星座旋转对称性的特点，降低 GA 单次迭代的计算复杂度。仿真结果表明，优化得到的DMs（DM set）显著改善了RDSM系统误比特率（bit error rate，BER）性能，对比随机搜索，低复杂度遗传算法有效提高了RDSMS的DMs优化效率，优化DMs所需的计算复杂度约为随机搜索的0.1%。

A genetic algorithm based method of optimizing dispersion matrix for RDSM system

Rectangular differential spatial modulation (RDSM) is a multi-antenna incoherent modulation technology with high spectral efficiency, low power consumption, and zero-overhead for channel estimation.RDSM is especially suitable for 6G communication systems, such as fast-moving Internet of vehicles, Internet of things, cellular networks, etc.However, the construction of the sparse rectangular unitary space-time dispersion matrix (DM) at transmitter is a problem.The proposed Genetic algorithm (GA) will result in less computational complexity than the currently used random research.The fitness of GA was calculated by the rank and determinant criterion (RDC) method to avoid discussions in differential system.Due to characteristics of constellation symbols of RDSM, the proposed method reduced the computational complexity during each single iteration in GA.The simulation results show that the optimized DMS can significantly improve the bit error rate (BER) performance of the RDSM system.Compared with random search, the low-complexity GA effectively improves the DMS optimization efficiency of RDSM.The computational complexity required for optimizing DMS is about 0.1% of random search optimization method.