论文标题
5G网络中的Noma大量物联网访问以及使用在线竞争力和学习
Massive IoT Access With NOMA in 5G Networks and Beyond Using Online Competitiveness and Learning
论文作者
论文摘要
本文研究了基于5G蜂窝互联网网络的在线用户分组，调度和电源分配的问题。由于试图授予网络的大量设备数量，因此采用了非正交的多个访问方法，以便在同一无线电资源块中容纳多个设备。与以前的大多数作品不同，目标是在分配传输功能的同时最大程度地提高服务设备的数量，以便尊重它们的实时要求以及有限的运行能源。首先，我们将一般问题提出为混合整数非线性程序（MINLP），在某些特殊情况下可以轻松地转换为MILP。其次，我们通过表征不同特殊情况的NP硬度来研究其计算复杂性。然后，通过将问题分为多个NOMA分组和调度子问题，提出了有效的在线竞争算法。此外，我们展示了如何使用这些在线算法，并将其解决方案结合在增强学习设置中，以获得权力分配，从而为问题提供了全球解决方案。我们的分析补充了模拟结果，以说明与最佳和最新方法相比，提出的算法的性能。
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.
