Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
It is well known that large scale multiple-input multiple-output (LS-MIMO) systems are very attractive technology to increase both spectral efficiency (SE) and energy efficiency (EE). However, one of big obstacles to the realization of the LS-MIMO system is the overhead of reference signals (RSs), s...
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doaj-42ef348b31254303ad6a95caa1b819752020-11-25T00:49:06ZengMDPI AGApplied Sciences2076-34172017-01-01719610.3390/app7010096app7010096Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO SystemsByung Moo Lee0Youngok Kim1School of Intelligent Mechatronic Engineering, Sejong University, Seoul 05006, KoreaDepartmemt of Electronic Engineering, Kwangwoon University, Seoul 01897, KoreaIt is well known that large scale multiple-input multiple-output (LS-MIMO) systems are very attractive technology to increase both spectral efficiency (SE) and energy efficiency (EE). However, one of big obstacles to the realization of the LS-MIMO system is the overhead of reference signals (RSs), since the number of RS increases as the number of transmitter (TX) antennas increases. In this paper, the RS overhead problem is analyzed, and we propose an RS overhead reduction scheme based on beam grouping, which is called beam grouping based resource reuse (BGRR). The proposed scheme divides one cell into several sectors and reuses the RS resources for the different sectors. The resource conflict is reduced using beam separability. According to the analysis and the simulation results, the proposed scheme can remarkably reduce the RS overhead and improve the SE performance significantly.http://www.mdpi.com/2076-3417/7/1/96reference signallarge scale MIMO systemresource reuse |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Byung Moo Lee Youngok Kim |
spellingShingle |
Byung Moo Lee Youngok Kim Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems Applied Sciences reference signal large scale MIMO system resource reuse |
author_facet |
Byung Moo Lee Youngok Kim |
author_sort |
Byung Moo Lee |
title |
Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems |
title_short |
Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems |
title_full |
Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems |
title_fullStr |
Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems |
title_full_unstemmed |
Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems |
title_sort |
beam grouping based rs resource reuse and de-contamination in large scale mimo systems |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2017-01-01 |
description |
It is well known that large scale multiple-input multiple-output (LS-MIMO) systems are very attractive technology to increase both spectral efficiency (SE) and energy efficiency (EE). However, one of big obstacles to the realization of the LS-MIMO system is the overhead of reference signals (RSs), since the number of RS increases as the number of transmitter (TX) antennas increases. In this paper, the RS overhead problem is analyzed, and we propose an RS overhead reduction scheme based on beam grouping, which is called beam grouping based resource reuse (BGRR). The proposed scheme divides one cell into several sectors and reuses the RS resources for the different sectors. The resource conflict is reduced using beam separability. According to the analysis and the simulation results, the proposed scheme can remarkably reduce the RS overhead and improve the SE performance significantly. |
topic |
reference signal large scale MIMO system resource reuse |
url |
http://www.mdpi.com/2076-3417/7/1/96 |
work_keys_str_mv |
AT byungmoolee beamgroupingbasedrsresourcereuseanddecontaminationinlargescalemimosystems AT youngokkim beamgroupingbasedrsresourcereuseanddecontaminationinlargescalemimosystems |
_version_ |
1725252995951624192 |