Evidence map›Paper›PMID 42356810›Full record

ArticleSensors (Basel, Switzerland)2026

Optimal IRS Allocation and Relay Selection for mmWave Multi-Hop Communications for Vehicular Sensor Data Sharing.

Xiaojun Yin, Xuyang Du, Xiaohan Wu, Xinming Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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.

Xiaojun YinSchool of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.
Xuyang DuSchool of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.
Xiaohan WuSchool of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.ORCID 0009-0007-2961-7257
Xinming ZhangSchool of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.ORCID 0000-0002-8136-6834

Funding

National Natural Science Foundation of China 62072425
6 · The paper itself

Abstract

Modern connected and automated vehicles are equipped with various onboard sensors, which continuously generate high-rate perception data. The reliable and timely sharing of such sensor data among neighboring vehicles requires high-capacity and low-latency vehicle-to-vehicle (V2V) communications. Millimeter-wave (mmWave) technology is a promising solution for supporting such high-rate transmission. However, mmWave V2V communication may be severely affected by non-line-of-sight (NLOS) blockage caused by limited transmission range, roadside obstacles, and moving vehicles. Relay forwarding can improve communication reliability and extend transmission distance, while intelligent reflecting surfaces (IRSs) can construct virtual line-of-sight (LOS) links to mitigate NLOS blockage. In this paper, we propose deploying IRSs on urban roadsides to improve mmWave multi-hop V2V communication for vehicular sensor-data sharing by integrating IRS-assisted link selection into multi-hop relay forwarding. However, IRS deployment introduces new challenges in relay selection and directional transmission coordination under interference. To address these challenges, we propose an IRS allocation and relay selection (IARS) scheme for IRS-assisted multi-hop V2V communication. The proposed scheme is based on a transmission evaluation function that jointly considers inter-vehicle distance, link quality, and concurrent transmissions. Simulation results show that the proposed IARS scheme can effectively improve communication reliability and reduce multi-hop delay, thereby supporting reliable and timely sensor-data sharing in urban vehicular networks.

Indexed as

cooperative perceptionIRSmillimeter-wave communicationrelay selectionsensor data sharingvehicular networks

Identifiers

PMID42356810
PMCPMC13306633

What OpenQuestion holds

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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.