Evidence map›Paper›PMID 38475143›Full record

ArticleSensors (Basel, Switzerland)2024

Clipping Noise Compensation for Overlapped Time Domain Multiplexing toward Low Peak-to-Average Power Ratio.

Chongda Huang, Qianzhen Zhang, Xiaoyuan Li, Yue Xiao

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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, top 98% of its field
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, 0 citations in OpenAlex.

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

4 authors at 2 institutions in 1 country.

Chongda HuangNational Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Qianzhen ZhangNational Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Xiaoyuan LiNational Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yue XiaoNational Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
University of Electronic Science and Technology of China · CNXidian University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Overlapped Time Domain Multiplexing (OvTDM) is a high-rate transmission technology that employs the idea of superposition coded modulation (SCM) scheme for signal generation, aiming to achieve maximum channel capacity sharing. Meanwhile, it is also widely considered as a promising technique toward physical layer security. As a main drawback of such system, a high peak-to-average power ratio (PAPR) issue in this system, arising from multi-layer superposition, can be addressed through intentional clipping. However, the detection at the receiver side is vulnerable to nonlinear distortion caused by clipping, which can degrade the performance. To mitigate this distortion, this paper proposed an iterative scheme for estimating and partially canceling clipping distortion at the receiver. We managed to mitigate the impact of clipping noise as much as possible and minimize the cost of optimizing PAPR, thereby improving the transmission performance of OvTDM in the context of amplitude clipping.

Indexed as

clipping distortion mitigationOverlapped Time Domain Multiplexing (OvTDM)peak-to-average power ratio (PAPR)superposition coded modulation (SCM)

Identifiers

PMID38475143
PMCPMC10934428
OpenAlexW4392385270

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.