Suppr超能文献

基于频分双工的无蜂窝大规模多输入多输出框架的可扩展性研究

On Scalability of FDD-Based Cell-Free Massive MIMO Framework.

作者信息

Hassan Beenish, Baig Sobia, Aslam Saad

机构信息

Department of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan.

Department of Electrical and Computer Engineering, Energy Research Center, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.

出版信息

Sensors (Basel). 2023 Aug 7;23(15):6991. doi: 10.3390/s23156991.

Abstract

Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free massive MIMO, a large coverage area corresponds to the provision and maintenance of the scalable quality of service requirements for an infinitely large number of UEs. The research in cell-free massive MIMO is mostly focused on time division duplex mode due to the availability of channel reciprocity which aids in avoiding feedback overhead. However, the frequency division duplex (FDD) protocol still dominates the current wireless standards, and the provision of angle reciprocity aids in reducing this overhead. The challenge of providing a scalable cell-free massive MIMO system in an FDD setting is also prevalent, since computational complexity regarding signal processing tasks, such as channel estimation, precoding/combining, and power allocation, becomes prohibitively high with an increase in the number of UEs. In this work, we consider an FDD-based scalable cell-free network with angular reciprocity and a dynamic cooperation clustering approach. We have proposed scalability for our FDD cell-free and performed a comparative analysis with reference to channel estimation, power allocation, and precoding/combining techniques. We present expressions for scalable spectral efficiency, angle-based precoding/combining schemes and provide a comparison of overhead between conventional and scalable angle-based estimation as well as combining schemes. Simulations confirm that the proposed scalable cell-free network based on an FDD scheme outperforms the conventional matched filtering scheme based on scalable precoding/combining schemes. The angle-based LP-MMSE in the FDD cell-free network provides 14.3% improvement in spectral efficiency and 11.11% improvement in energy efficiency compared to the scalable MF scheme.

摘要

无小区大规模多输入多输出(MIMO)系统有潜力提供联合服务,包括联合初始接入、接入点(AP)的高效聚类以及在大覆盖区域向用户设备(UE)进行导频分配,同时减少干扰。在无小区大规模MIMO中,大覆盖区域对应于为无限数量的UE提供并维持可扩展的服务质量要求。由于信道互易性的存在有助于避免反馈开销,无小区大规模MIMO的研究大多集中在时分双工模式。然而,频分双工(FDD)协议仍主导着当前的无线标准,而角度互易性的提供有助于减少这种开销。在FDD设置中提供可扩展的无小区大规模MIMO系统的挑战也很普遍,因为随着UE数量的增加,诸如信道估计、预编码/合并和功率分配等信号处理任务的计算复杂度会变得过高。在这项工作中,我们考虑一个基于FDD的具有角度互易性和动态协作聚类方法的可扩展无小区网络。我们为基于FDD的无小区系统提出了可扩展性,并参考信道估计、功率分配和预编码/合并技术进行了比较分析。我们给出了可扩展频谱效率的表达式、基于角度的预编码/合并方案,并比较了传统和可扩展的基于角度的估计以及合并方案之间的开销。仿真证实,所提出的基于FDD方案的可扩展无小区网络优于基于可扩展预编码/合并方案的传统匹配滤波方案。与可扩展MF方案相比,FDD无小区网络中基于角度的线性迫零最小均方误差(LP-MMSE)在频谱效率上提高了14.3%,在能量效率上提高了11.11%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d398/10422490/623da9b94457/sensors-23-06991-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验