The mode selection and resource allocation in fog radio access networks
(F-RANs) have been advocated as key techniques to improve spectral and energy
efficiency. In this paper, we investigate the joint optimization of mode
selection and resource allocation in uplink F-RANs, where both of the
traditional user equipments (UEs) and fog UEs are served by constructed network
slice instances. The concerned optimization is formulated as a mixed-integer
programming problem, and both the orthogonal and multiplexed subchannel
allocation strategies are proposed to guarantee the slice isolation. Motivated
by the development of machine learning, two reinforcement learning based
algorithms are developed to solve the original high complexity problem under
traditional and fog UEs' specific performance requirements. The basic idea of
the proposals is to generate a good mode selection policy according to the
immediate reward fed back by an environment. Simulation results validate the
benefits of our proposed algorithms and show that a tradeoff between system
power consumption and queue delay can be achieved.

本文研究了雾射频接入网络（F-RAN）中的模式选择和资源分配，并采用混合整数规划方法优化性能。此外，基于机器学习的强化学习算法用于解决原始高复杂性问题。最终的仿真结果表明，我们提出的算法可以在系统功耗和队列延迟之间实现权衡。