基于Q学习的自主联合无线资源管理算法

A Q-learning Based Autonomic Joint Radio Resource Management Algorithm

该文提出了一种基于Q学习的联合无线资源管理(JRRM)算法,用于异构无线接入技术条件下B3G系统的自主资源优化。JRRM控制器通过与无线环境的"试错"交互,学会为每个会话分配合适的接入技术和业务带宽。为降低存储需求,算法引入了反向传播神经网络用于泛化其输入状态空间。仿真结果表明,该算法不仅通过在线学习实现了JRRM的自主化,且在频谱效用和阻塞率之间获得了很好的性能折衷。

A Q-learning based Joint Radio Resource Management （JRRM） algorithm is proposed for the autonomic resource optimization in a B3G system with heterogeneous Radio Access Technologies （RAT）. Through the ＂trial-and-error＂ interactions with the radio environment, the JRRM controller learns to allocate the proper RAT and the service bandwidth for each session. A backpropagation neural network is adopted to generalize the large input state space to reduce memory requirement. Simulation results show that the proposed algorithm not only realizes the autonomy of JRRM through the online learning process, but also achieves well trade-off between the spectrum utility and the blocking probability.