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.