面向用户需求的低轨卫星资源分配算法

Resource allocation algorithm for low earth orbit satellites oriented to user demand

低轨(LEO)卫星多波束通信场景下,传统固定资源分配算法无法满足不同用户对信道容量的差异需求。以适应用户需求分配为主要目标,建立联合信道分配、带宽分配和功率分配的最小供需差优化模型,并引入图样分割多址接入技术(PDMA)提升信道资源的利用率。针对该模型的非凸特性,通过Q-learning算法学习资源分配最优策略为每个用户分配适合的信道容量,并引入奖励阈值进一步改进算法,加快算法的收敛,且使算法达到收敛时供需差异更小。仿真结果表明,改进后的算法收敛速度约是改进前的3.33倍:改进算法能满足更大的用户需求,比改进前Qlearning算法提升14%,是传统固定算法的2.14倍。

In Low Earth orbit(LEO)satellite multi-beam communication scenario,the traditional fixed resource allocation algorithm can not meet the differences in channel capacity requirements of different users.In order to meet the requirements of users,the optimization model of minimum supply-demand difference of combining channel allocation,bandwidth allocation and power allocation was established,and Pattern Division Multiple Access technology(PDMA)was introduced to improve the utilization of channel resources.In view of the non-convex characteristic of the model,the optimal resource allocation strategy learned by the Q-learning algorithm was used to allocate the channel capacity suitable for each user,and a reward threshold was introduced to further improve the algorithm,speeding up the convergence and minimizing the difference between supply and demand when the algorithm converged.The simulation results show that the convergence speed of the improved algorithm is about 3.33 times that before improvement;the improved algorithm can meet larger user requirement,about 14%higher than the Q-learning algorithm before improvement,about 2.14 times that of the traditional fixed algorithm.