Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications.

Duo Zhang, Wen Wu, Dagang Fang, Wenqin Wang, Can Cui

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Duo ZhangMinisterial Key Laboratory of JGMT, School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China. duozhang@foxmail.com.

Wen WuMinisterial Key Laboratory of JGMT, School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China. wuwen@mail.njust.edu.cn.

Dagang FangMinisterial Key Laboratory of JGMT, School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China. fangdg@mail.njust.edu.cn.

Wenqin WangSchool of Communication and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China. wqwang@uestc.edu.cn.

Can CuiMinisterial Key Laboratory of JGMT, School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing 210094, China. cuican100@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In modern communication and radar applications, large-scale sensor arrays have increasingly been used to improve the performance of a system. However, the hardware cost and circuit power consumption scale linearly with the number of sensors, which makes the whole system expensive and power-hungry. This paper presents a low-cost nested multiple-input multiple-output (MIMO) array, which is capable of providing O ( 2 N 2 ) degrees of freedom (DOF) with O ( N ) physical sensors. The sensor locations of the proposed array have closed-form expressions. Thus, the aperture size and number of DOF can be predicted as a function of the total number of sensors. Additionally, with the help of time-sequence-phase-weighting (TSPW) technology, only one receiver channel is required for sampling the signals received by all of the sensors, which is conducive to reducing the hardware cost and power consumption. Numerical simulation results demonstrate the effectiveness and superiority of the proposed array.

Indexed as

degrees of freedom (DOF)large-scale arraymultiple-input multiple-output (MIMO)nested arraysingle receiver channeltime-sequence-phase-weighting (TSPW)

Identifiers

PMID28498329

DOI10.3390/s17051105

PMCPMC5470495

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.