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采用混合波束赋形的毫米波蜂窝系统的小区选择技术。

Cell Selection Technique for Millimeter-Wave Cellular Systems with Hybrid Beamforming.

机构信息

School of Electrical and Electronic Engineering, Chung-Ang University, Seoul 156-756, Korea.

出版信息

Sensors (Basel). 2017 Jun 21;17(6):1461. doi: 10.3390/s17061461.

DOI:10.3390/s17061461
PMID:28635636
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5492134/
Abstract

In this paper, a cell selection technique for millimeter-wave (mm-wave) cellular systems with hybrid beamforming is proposed. To select a serving cell, taking into account hybrid beamforming structures in a mm-wave cellular system, the angles of arrival and departure for all candidate cells need to be estimated in the initialization stage, requiring a long processing time. To enable simultaneous multi-beam transmissions in a multi-cell environment, a cell and beam synchronization signal (CBSS) is proposed to carry beam IDs in conjunction with cell IDs. A serving cell maximizing the channel capacity of the hybrid beamformer is selected with the estimated channel information and the optimum precoder. The performance of the proposed technique is evaluated by a computer simulation with a spatial channel model in a simple model of a mm-wave cellular system. It is shown by simulation that the proposed technique with the CBSS can significantly reduce the processing time for channel estimation and cell selection, and can achieve additional gains in channel capacity, or in bit error rate, compared to that obtained by conventional techniques.

摘要

本文提出了一种用于具有混合波束成形的毫米波 (mmWave) 蜂窝系统的小区选择技术。为了选择服务小区,在考虑 mmWave 蜂窝系统中的混合波束成形结构时,在初始化阶段需要估计所有候选小区的到达和离开角度,这需要很长的处理时间。为了在多小区环境中实现同时多波束传输,提出了一种小区和波束同步信号 (CBSS),该信号与小区 ID 一起承载波束 ID。使用估计的信道信息和最优预编码器,选择使混合波束成形器的信道容量最大化的服务小区。通过在 mmWave 蜂窝系统的简单模型中的空间信道模型进行计算机仿真,评估了所提出的技术的性能。仿真结果表明,与传统技术相比,使用 CBSS 的所提出的技术可以显著减少信道估计和小区选择的处理时间,并在信道容量或误比特率方面获得额外的增益。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/f1d5dc13e563/sensors-17-01461-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/3314b1aa5383/sensors-17-01461-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/d21dd6c01003/sensors-17-01461-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/6a124960080e/sensors-17-01461-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/8123b3050716/sensors-17-01461-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/3ed9f4310b25/sensors-17-01461-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/77e3bf4d5ce2/sensors-17-01461-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/f0ba918199dc/sensors-17-01461-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/f1d5dc13e563/sensors-17-01461-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/3314b1aa5383/sensors-17-01461-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/d21dd6c01003/sensors-17-01461-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/6a124960080e/sensors-17-01461-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/8123b3050716/sensors-17-01461-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/3ed9f4310b25/sensors-17-01461-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/77e3bf4d5ce2/sensors-17-01461-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/f0ba918199dc/sensors-17-01461-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ced3/5492134/f1d5dc13e563/sensors-17-01461-g008.jpg

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