时分波分无源光网络与云无线接入网联合架构中负载平衡的用户关联与资源分配策略

在时分波分无源光网络(TWDM-PON)与云无线接入网(C-RAN)的联合架构中，由于无线域的负载不均衡问题，限制了网络整体的传输效率。为了充分利用TWDM-PON与C-RAN联合架构的网络资源，并保证用户的服务质量(QoS)，该文提出一种负载平衡的用户关联与资源分配算法(LBUARA)。首先根据不同用户的服务质量需求以及分布式无线射频头端(RRH)的负载对用户的影响，构建用户收益函数。进而，在保证用户服务质量的前提下，根据网络状态建立随机博弈模型，并基于多智能体Q学习提出负载均衡的用户关联和资源分配算法，从而获得最优的用户关联与资源分配方案。仿真结果表明，所提的用户关联和资源分配策略能够实现网络的负载均衡，保证用户的服务质量，并提高网络吞吐量。

Load Balancing User Association and Resource Allocation Strategy in Time and Wavelength Division Multiplexed Passive Optical Network and Cloud Radio Access Network Joint Architecture

The load imbalance in the wireless domain limits the overall transmission efficiency of the network in the joint architecture of Time and Wavelength Division Multiplexed Passive Optical Network (TWDM-PON) and Cloud Radio Access Network (C-RAN). A Load Balancing User Association and Resource Allocation (LBUARA) algorithm is proposed to ensure the Quality of Service(QoS) of users, and make full use of network resources TWDM-PON jointly with C-RAN architecture. Firstly, the user revenue function is constructed according to the service quality requirements of different users and the impact of Remote Radio Head (RRH) load on users. Furthermore, a random game model is established according to the network state, under the premise of ensuring the quality of user service. A user association and resource allocation algorithm based on multi-agent Q-learning load balancing is proposed to obtain the optimal user association and resource allocation plan. The simulation results show that users association and resource allocation strategies mentioned can achieve load balancing network to ensure quality of service users, and improve network throughput.