接入与回传一体化小基站的接入控制与资源分配联合优化算法

Joint Admission Control and Resource Allocation Algorithm for Access and Backhaul Integrated Small Base Station

针对全双工无线接入与回传一体化小基站场景下长期的频谱效率和能效同时最大化问题,该文提出一种基于近似动态规划理论的接入与回传一体化小基站接入控制与资源分配联合优化算法。该算法首先联合考虑当前基站的资源使用和功率配置情况,在任一用户需求动态到达以及平均时延、小基站回传速率和传输功率约束下,使用受限马尔科夫决策过程(CMDP)建立频谱效率最大化和功率消耗最小化的多目标优化模型,其次运用切比雪夫理论将多目标优化问题转化为单目标问题,并使用拉格朗日对偶分解法进一步转化为非受限的马尔科夫决策过程(MDP)问题。最后,为了解决其求解时存在的“维度灾”爆炸问题,该文提出基于近似动态规划的无线接入与回传一体化小基站资源动态分配算法进行求解,得到此时的接入与资源分配策略。仿真结果表明,所提算法能在保证平均时延约束、小基站回传速率约束和传输功率约束的同时最大化长期平均频谱效率和能效。

To maximize the long-term spectral efficiency and energy efficiency of a full duplex wireless access and backhaul integrated small base station scene, approximate dynamic programming based joint admission control and resource allocation optimization algorithm is proposed. The algorithm firstly considers the resource usage and power configuration of the current base station, the dynamic demand of user, the constraints of average delay as well as backhaul rate and transmission power. The corresponding multi-objective optimization model of maximum spectrum efficiency and minimizes power consumption is established by using the Constrained Markov Decision Process(CMDP). Then, the Chebyshev theory is used to transform the multiobjective into a single-objective optimization, and the Lagrange dual decomposition method is then used to convert the single-objective problem into unrestricted Markov decision process problem. Finally, To solve the “dimension disaster” explosion that generated when solving this unrestricted Markov Decision Process(MDP) problem, a dynamic resource allocation algorithms based on approximate dynamic programming is presented, and the access and resource allocation strategy is obtained during this process. The simulation results show that the algorithm can maximize the long-term average spectrum efficiency and energy efficiency, within the constraints of the average delay, backhaul rate and transmission power, under the scenario of integrated access and backhaul small base station.