Evidence map›Paper›PMID 40942934›Full record

ReviewSensors (Basel, Switzerland)2025

On the Role of Artificial Intelligent Technology for Millimetre-Wave and Terahertz Applications.

Lida Kouhalvandi, Ladislau Matekovits

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Lida KouhalvandiDepartment of Electrical and Electronics Engineering, Dogus University, 34775 Istanbul, Türkiye.ORCID 0000-0003-0693-4114
Ladislau MatekovitsDepartment of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy.ORCID 0000-0003-0946-9561

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Next-generation wireless communication networks are developing across the world day by day; this requires high data rate transportation over the systems. Millimeter-wave (mm-wave) spectrum with terahertz (THz) bands is a promising solution for next-generation systems that are able to meet these requirements effectively. For such networks, designing new waveforms, providing high-quality service, reliability, energy efficiency, and many other specifications are taking on important roles in adapting to high-performance communication systems. Recently, artificial intelligence (AI) and machine learning (ML) methods have proved their effectiveness in

Indexed as

antennaartificial intelligence (AI)millimeter-wave (mm-wave)neural network (NN)optimizationsixth-generation (6G)terahertz (THz)

Identifiers

PMID40942934
PMCPMC12431012

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.