自适应遗传算法下的NOMA用户动态分簇

NOMA(Non-orthogonal Multiple Access,NOMA)系统中的用户分簇策略对系统性能有着极大的影响。该文主要研究NOMA下行链路的用户动态分簇问题,其目的是最大化系统总吞吐量。与大多数文章不同,该研究对簇中用户数以及簇个数都没有限制。遗传算法可用于优化NOMA网络中的用户动态分簇,但标准遗传算法存在收敛速度慢且容易陷入局部最优的问题。基于此,该文将自适应调节参数的改进遗传算法用于用户的动态分簇,来改善上述问题。仿真结果表明,该算法相比于穷举搜索能够有效降低求解复杂度,且系统性能明显优于固定簇分配算法及自适应配对策略下的系统性能。 

User Dynamic Clustering in Downlink NOMA Based on Adaptive Genetic Algorithm

The user clustering strategy in NOMA (Non-orthogonal Multiple Access, NOMA) system has a great impact on system performance.This paper mainly studied the user clustering problem of NOMA downlink, and its main purpose was to maximize the total system throughput.The difference from most previous articles was that this study did not limit the number of users in a cluster and the number of clusters.The standard genetic algorithm can be used to optimize the dynamic clustering of users in the NOMA network, but it has the problem of slow convergence and easy to fall into local optimum.Based on this, this paper used an improved genetic algorithm with adaptive adjustment parameters for dynamic clustering of users to improve the above problems.The simulation results show that compared with exhaustive search, the algorithm can effectively reduce the complexity of the solution, and the system performance is significantly better than the system performance under the fixed cluster allocation algorithm and adaptive matching strategy.