LEO-RAN切片场景联合用户关联和动态资源分配算法

Joint user association and dynamic resource allocation algorithm for LEO-RAN slicing scenarios

为了解决6G天地一体化网络的资源高效利用问题,提出了一种面向高密度低地球轨道卫星-无线接入网(LEO-RAN)切片场景的联合用户关联和动态资源分配算法。考虑不同切片的最小速率、最大时延及资源比例等约束,以频谱效率(SE)和不同切片服务水平协议(SLA)满意率(SSR)的加权和作为优化目标,建立用户关联和资源分配的联合优化问题。首先设计基于多智能体深度确定性策略梯度(MADDPG)的网络切片算法确定切片资源比例,然后采用基于拉格朗日对偶的用户关联算法确定最优的用户关联策略,最后通过轮询调度机制将资源分配给用户。仿真结果表明,所提算法在满足不同切片差异化SLA的同时能够有效提高SE。与基于MADDPG-RA、MATD3-LG、MATD3-RA、MASAC-LG和MASAC-RA算法相比,所提算法系统效用分别提升了2.0%、2.3%、5.7%、8.7%和9.4%。

A joint user association and dynamic resource allocation algorithm was proposed for the slicing scenario of ultra dense low earth orbit-radio access network(LEO-RAN)in order to address the efficient utilization of resources of the integrated terrestrial-satellite network for 6G.Considering the constraints of the minimum rate,maximum delay and resource proportion of different slices,a joint optimization problem of user association and resource allocation was established to maximize the weighted sum of the SE and the differentiated SLA of different slices as the optimization objective.A network slicing algorithm based on multi-agent deep deterministic policy gradient(MADDPG)was proposed to determine the proportion of slicing resources,a Lagrange dual based user association algorithm was proposed to determine the optimal user association policy and the resources were allocated to users by using the round-robin scheduling mechanism.The simulation results show that the proposed algorithm can effectively improve SE while satisfying the differentiated SLA of different slices.Compared with MADDPG-RA,MATD3-LG,MATD3-RA,MASAC-LG and MASAC-RA algorithms,the system utility of the proposed algorithm is improved by 2.0%,2.3%,5.7%,8.7%and 9.4%,respectively.