宽带系统双向训练

Jialing Liu, Q. Cheng, W. Xiao, Diana Maamari, A. Soong
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引用次数: 0

摘要

为了进一步提高5G大规模MIMO网络的频谱效率,开发了TDD系统的双向训练(BiT),以最大限度地提高下行加权和速率。然而,以前的工作仅限于窄带系统。在本文中,我们将比特扩展到5G宽带系统。首先提出了宽带系统的全局集中优化问题。然后将(次优)解决方案分布在基站和用户设备(UE)上,形成宽带比特算法,该算法仅使用本地信息迭代地为每个基站和每个UE调整传输和接收滤波器。该算法可以看作是一个窄带比特,操作于一组具有不同信道的子载波的最优窄带表示,并且最优窄带表示保持所有信道的第一和第二矩。仿真结果验证了该算法在宽带系统中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bi-Directional Training for Wideband Systems
To further improve the spectrum efficiency of 5G massive MIMO networks, bi-directional training (BiT) was developed for TDD systems to maximize the downlink weighted sum rate. However, the previous work was limited to narrowband systems. In this paper, we extend BiT for 5G wideband systems. A global, centralized optimization problem is first formulated for a wideband system. The (sub-optimal) solution is then distributed across the base stations and user equipment (UE), resulting into a wideband BiT algorithm that iteratively adapts transmission and reception filters for each base station and each UE with only local information. The algorithm may be seen as a narrowband BiT operating on an optimal narrowband representation of a group of subcarriers each with a different channel, and the optimal narrowband representation maintains the first and second moments of all the channels. Simulation results are provided to evaluate the performance of the algorithm in a wideband system.
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