Reinforcement Learning-Based Joint User Pairing and Power Allocation in MIMO-NOMA Systems

Sensors

Jaehee Lee¹  Jaewoo So¹ 

[1] Department of Electronic Engineering, Sogang University, Seoul 04107, Korea;

关键词: non-orthogonal multiple access;    multiple-input multiple-output;    user pairing;    power allocation;    reinforcement learning;   

DOI  :  10.3390/s20247094

来源: DOAJ

【 摘 要 】

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).

【 授权许可】

Unknown