Evidence map›Paper›PMID 42045308›Full record

ArticleScientific reports2026

Optimized weight spectrum method for interference signal separation in FSO communication.

Zhengguang Duan, Shengzhi Qiang, Haonian Zhao, Yuanqing Wang

Abstract read
In one paragraph

Article in Scientific reports, 2026. 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

4 authors.

Zhengguang DuanSchool of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China. duan2071@163.com.
Shengzhi QiangSchool of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China.
Haonian ZhaoSchool of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China.
Yuanqing WangSchool of Electronic Science and Engineering, Nanjing University, Nanjing, 210023, China.

Funding

The Scientific Research Program of Anhui Provincial Department of Education 2023AH040336
6 · The paper itself

Abstract

Free Space Optical (FSO) Communication, as a novel communication method, has advantages in terms of interference resistance and confidentiality compared to radio frequency (RF) technology. Since the spectrum of voice signals in FSO communication is confined to a specific range, thereby inherently limiting the range of frequencies that can be processed in the frequency domain. To enhance the noise resistance of voice signals in communications, we employed a method based on optimized weight spectra to identify and remove noise from signals. The sample signal is transformed into the frequency domain via Fourier transform, where noise is removed by applying an optimized weighting spectrum. Subsequently, the signal is reconstructed through inverse Fourier transform. The designed algorithm analyzed the bit error rate (BER) under different signal-to-noise ratios through simulation. Using the developed optical communication equipment at a distance of 80 m outdoors to test the BER of different modulation methods, the optimized weighted spectrum has a significantly lower BER, which is lower than 9.31 ‰. In this study, compared to the method of directly retaining effective frequency components, the BER significantly decreases, and the ability of noise reduction and anti-interference is notably improved. The use of new modulation techniques enhanced the reliability of voice information transmission, demonstrated potential applicability, and improved the environmental adaptability of FSO communication systems.

Indexed as

Convex optimizationFSONoise removalOptimized weights spectrum

Identifiers

PMID42045308
PMCPMC13121601

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Registered trials

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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.