论文标题
物联网网络中NOMA的竞争算法和增强学习
Competitive Algorithms and Reinforcement Learning for NOMA in IoT Networks
论文作者
论文摘要
本文使用非正交多访问（NOMA）技术研究了第五代（B5G）网络中大量物联网（IoT）访问的问题。该问题涉及大量的物联网设备分组和功率分配，以尊重低潜伏期以及物联网设备的有限运行能量。所考虑的目标函数最大化成功接收的IoT数据包的数量与经典与总和相关的目标函数不同。该问题首先分为多个Noma分组子问题。然后，使用竞争分析，提出了有效的在线竞争算法（CA）来解决每个子问题。接下来，为了解决功率分配问题，我们提出了一个新的增强学习（RL）框架，其中RL代理学会将CA用作黑匣子，并将获得的解决方案结合到每个子问题上，以确定每个NOMA组的功率分配。我们的仿真结果表明，提出的创新RL框架的表现优于深Q学习方法，并且是近距离的。
This paper studies the problem of massive Internet of things (IoT) access in beyond fifth generation (B5G) networks using non-orthogonal multiple access (NOMA) technique. The problem involves massive IoT devices grouping and power allocation in order to respect the low latency as well as the limited operating energy of the IoT devices. The considered objective function, maximizing the number of successfully received IoT packets, is different from the classical sum-rate-related objective functions. The problem is first divided into multiple NOMA grouping subproblems. Then, using competitive analysis, an efficient online competitive algorithm (CA) is proposed to solve each subproblem. Next, to solve the power allocation problem, we propose a new reinforcement learning (RL) framework in which a RL agent learns to use the CA as a black box and combines the obtained solutions to each subproblem to determine the power allocation for each NOMA group. Our simulations results reveal that the proposed innovative RL framework outperforms deep-Q-learning methods and is close-to-optimal.
