This paper studies the problem of online user grouping, scheduling and power
allocation in beyond 5G cellular-based Internet of things networks. Due to the
massive number of devices trying to be granted to the network, non-orthogonal
multiple access method is adopted in order to accommodate multiple devices in
the same radio resource block. Different from most previous works, the
objective is to maximize the number of served devices while allocating their
transmission powers such that their real-time requirements as well as their
limited operating energy are respected. First, we formulate the general problem
as a mixed integer non-linear program (MINLP) that can be transformed easily to
MILP for some special cases. Second, we study its computational complexity by
characterizing the NP-hardness of different special cases. Then, by dividing
the problem into multiple NOMA grouping and scheduling subproblems, efficient
online competitive algorithms are proposed. Further, we show how to use these
online algorithms and combine their solutions in a reinforcement learning
setting to obtain the power allocation and hence the global solution to the
problem. Our analysis are supplemented by simulation results to illustrate the
performance of the proposed algorithms with comparison to optimal and
state-of-the-art methods.

本文研究了超出 5G 基于蜂窝的物联网网络中在线用户分组、调度和功率分配的问题。采用非正交多址接入方法来容纳多个设备在相同的无线电资源块中。作者提出了高效的在线竞争算法，并演示了如何在强化学习设置中使用这些在线算法并组合它们的解来获得功能强大的动力分配和全局解决方案。