Soumana Hamadou Abdel Nasser, Wa Maina Ciira, Soidridine Moussa Moindze
Department of Electrical Engineering, Pan African University Institute for Basic Science, Technology and Innovation (PAUSTI), Juja, Nairobi, Kenya.
Center for Data Science Artificial Intelligence (DSAIL), Dedan Kimathi University of Technology (DeKUT), Nyeri, Kenya.
Heliyon. 2024 Jun 20;10(12):e33175. doi: 10.1016/j.heliyon.2024.e33175. eCollection 2024 Jun 30.
The effectiveness of developing reconfigurable intelligent surfaces (RIS) for heterogeneous network (HetNet) systems has resulted in significant spectral efficiency (SE) gains. The majority of current research has not addressed effectively whether a hybrid metaheuristic technique may be used to create the hybrid RIS phase changes in HetNet. In this paper, we study a heterogeneous network (HetNet) assisted by a hybrid reconfigurable intelligent surface (H-RIS). Compared with the passive RIS, a hybrid RIS that has only 8% active elements is proposed to enhance by reflecting and amplifying incident signals. Hybrid RIS phase shift and SBS transmit beamforming are optimised. At the SBS, transmit beamforming based on zero-forcing is employed, whereas for hybrid RIS phase shift optimisation, the hybrid PSO-GWO (HPSOGWO) method is employed. The exploitation power of particle swarm optimisation (PSO) and the exploration power of the grey wolf optimizer (GWO) are combined in this hybrid approach. Simulation findings show that the suggested method, which uses only modest active RIS elements, can achieve a significant spectral efficiency improvement over both RIS-aided HetNet with totally passive RIS elements but with the SDR method to optimise the RIS phase changes and RIS-aided HetNet with entirely active RIS elements but with the Lagrange multiplier (LM) scheme to design the phase shift at the active RIS.
为异构网络（HetNet）系统开发可重构智能表面（RIS）的有效性已带来显著的频谱效率（SE）提升。当前的大多数研究尚未有效解决是否可使用混合元启发式技术来创建HetNet中的混合RIS相变问题。在本文中，我们研究了一种由混合可重构智能表面（H-RIS）辅助的异构网络（HetNet）。与无源RIS相比，提出了一种仅具有8%有源元件的混合RIS，通过反射和放大入射信号来增强性能。对混合RIS相移和SBS发射波束成形进行了优化。在SBS处，采用基于迫零的发射波束成形，而对于混合RIS相移优化，采用混合粒子群优化-灰狼优化器（HPSOGWO）方法。这种混合方法结合了粒子群优化（PSO）的开发能力和灰狼优化器（GWO）的探索能力。仿真结果表明，所提出的方法仅使用少量有源RIS元件，与完全无源RIS元件但采用软件定义无线电（SDR）方法优化RIS相变的RIS辅助HetNet以及完全有源RIS元件但采用拉格朗日乘数（LM）方案设计有源RIS处相移的RIS辅助HetNet相比，能够实现显著的频谱效率提升。
