Chen Liang, Sefat Shebnam M, Kim Ki-Il
Jilin Provincial Institute of Education, Chang Chun 130022, China.
Department of Computer Science, Independent University, Bangladesh.
Heliyon. 2023 Jun 23;9(6):e17580. doi: 10.1016/j.heliyon.2023.e17580. eCollection 2023 Jun.
Fifth generation (5G) wireless networks are based on the use of spectrum blocks above 6 GHz in the millimeter wave (mmWave) range to increase throughput and reduce the overall level of interference in very busy frequency bands below 6 GHz. With the global deployment of the first commercial installations of 5G, the availability of multi-Gbps wireless connections in the mmWave frequency band becomes closer to reality and opens up some unique uses for 5G. Although, mmWave communication is expected to enable high-power radio links and broadband wireless intranet, its main challenges are inherent poor propagation conditions and high transmitter-receiver coordination requirement, which prevent it from realizing its full potential. When smart reflective surfaces are used in mmWave communication, channel state information becomes complex and imprecise. In this study, a hybrid intelligent reflecting surface consisting of a large number of passive components and a small number of RF circuits is proposed as a solution. Then, an improved deep neural network (DNN)-based technique is proposed to estimate the effective channel. The proposed technique provides better channel estimation performance according to the simulation results and improves the quality of service.
第五代（5G）无线网络基于使用毫米波（mmWave）范围内6GHz以上的频谱块，以提高吞吐量并降低6GHz以下非常繁忙频段中的整体干扰水平。随着5G首次商业安装在全球的部署，毫米波频段中多吉比特无线连接的可用性越来越接近现实，并为5G开辟了一些独特的用途。尽管毫米波通信有望实现高功率无线链路和宽带无线内部网，但其主要挑战是固有的传播条件差和收发器协调要求高，这阻碍了它充分发挥潜力。当智能反射面用于毫米波通信时，信道状态信息变得复杂且不准确。在本研究中，提出了一种由大量无源组件和少量射频电路组成的混合智能反射面作为解决方案。然后，提出了一种改进的基于深度神经网络（DNN）的技术来估计有效信道。根据仿真结果，所提出的技术提供了更好的信道估计性能，并提高了服务质量。
