Liang Weiqian, Abdrabou Atef, Orumwense Efe Francis, Madsen Dag Øivind
School of Ocean Information Engineering, Jimei University, Xiamen, Fujian, 361021, China.
Department of Electrical and Electronic Engineering, Cape Peninsula University of Technology, Symphony Way, Bellville Campus, South Africa.
Heliyon. 2024 Jan 24;10(3):e25107. doi: 10.1016/j.heliyon.2024.e25107. eCollection 2024 Feb 15.
The effectiveness of implementing intelligent reflecting surface (IRS) for millimeter-wave (mmWave)-non-orthogonal multiple-access (NOMA) systems has allowed for significant sum-rate improvements. The majority of recent research has not discussed how well the IRS-mmWave-NOMA combination performs. Therefore, a new technique for resource optimization in IRS-mmWave-NOMA B5G wireless networks is proposed in this research. The key concept is to use an iterative algorithm to solve the optimization issue while incorporating many crucial constraints like the selection of the IRS beam, transmit power distribution, and decoding order, among others. Simulation results show that the proposed approach outperforms existing state-of-the-art algorithms in terms of computation delay, sum rate and NMSE. The computational complexity also validated the simplicity and hardware-friendly feature of the proposed algorithm.
在毫米波非正交多址接入（mmWave-NOMA）系统中实施智能反射面（IRS）的有效性已实现了显著的和速率提升。近期的大多数研究尚未讨论IRS-mmWave-NOMA组合的性能究竟如何。因此，本研究提出了一种用于IRS-mmWave-NOMA B5G无线网络资源优化的新技术。关键概念是使用迭代算法来解决优化问题，同时纳入诸如IRS波束选择、发射功率分配和解码顺序等许多关键约束条件。仿真结果表明，所提出的方法在计算延迟、和速率和归一化均方误差方面优于现有的最先进算法。计算复杂度也验证了所提算法的简单性和硬件友好特性。
