基于遗传算法的混叠式非正交多址接入方法

In order to improve the resource utilization of sparse code multiple access system (SCMA), a hybrid non-orthogonal multiple access (NOMA) method based on genetic algorithm is proposed. This method makes use of the overload characteristics of NOMA, and allows the same resource unit to carry both scheduled access and random competitive access services simultaneously, thus realizing the fine-grained integration of the two access modes. Furthermore, a hybrid NOMA resource allocation algorithm based on genetic algorithm is designed. Taking the total capacity of the two access modes as the optimization objective and the fitness of genetic algorithm, the resource allocation effect is optimized through multiple iterations of crossover and mutation operations. Simulation results show that compared with other methods, the proposed method can achieve higher throughput performance in various scenarios, effectively support scheduling access and random competitive access, and improve resource utilization of NOMA system.