Lee Jaehee, So Jaewoo
Department of Electronic Engineering, Sogang University, Seoul 04107, Korea.
Sensors (Basel). 2020 Dec 11;20(24):7094. doi: 10.3390/s20247094.
In this paper, we consider a multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) system with reinforcement learning (RL). NOMA, which is a technique for increasing the spectrum efficiency, has been extensively studied in fifth-generation (5G) wireless communication systems. The application of MIMO to NOMA can result in an even higher spectral efficiency. Moreover, user pairing and power allocation problem are important techniques in NOMA. However, NOMA has a fundamental limitation of the high computational complexity due to rapidly changing radio channels. This limitation makes it difficult to utilize the characteristics of the channel and allocate radio resources efficiently. To reduce the computational complexity, we propose an RL-based joint user pairing and power allocation scheme. By applying Q-learning, we are able to perform user pairing and power allocation simultaneously, which reduces the computational complexity. The simulation results show that the proposed scheme achieves a sum rate similar to that achieved with the exhaustive search (ES).
在本文中，我们考虑一个具有强化学习（RL）的多输入多输出（MIMO）-非正交多址接入（NOMA）系统。NOMA作为一种提高频谱效率的技术，已在第五代（5G）无线通信系统中得到广泛研究。将MIMO应用于NOMA可带来更高的频谱效率。此外，用户配对和功率分配问题是NOMA中的重要技术。然而，由于无线信道快速变化，NOMA存在计算复杂度高这一基本限制。这一限制使得难以利用信道特性并高效分配无线资源。为降低计算复杂度，我们提出一种基于RL的联合用户配对和功率分配方案。通过应用Q学习，我们能够同时进行用户配对和功率分配，从而降低计算复杂度。仿真结果表明，所提方案实现的和速率与穷举搜索（ES）方案实现的和速率相近。
