ReviewHeliyon2022
A review on machine learning and deep learning for various antenna design applications.
Review in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Who cites it
15 citing papers in PubMed.
- Machine learning-optimized compact wearable frequency reconfigurable antenna for sub-6 GHz/mm-wave 5G integration.Scientific reports · 2025Article
- ML/GA-based performance optimization of PBG-enhanced THz microstrip patch antennas on PTFE-SWCNT.Scientific reports · 2025Article
- On the Role of Artificial Intelligent Technology for Millimetre-Wave and Terahertz Applications.Sensors (Basel, Switzerland) · 2025Review
- Machine learning-optimized dual-band wearable antenna for real-time remote patient monitoring in biomedical IoT systems.Scientific reports · 2025Article
- Metamaterial based tri-band compact MIMO antenna system for 5G IoT applications with machine learning performance verification.Scientific reports · 2025Article
- The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports.PloS one · 2025Article
- A Deep Learning Framework for Evaluating the Over-the-Air Performance of the Antenna in Mobile Terminals.Sensors (Basel, Switzerland) · 2024Article
- Article
- Functional metamaterials for wireless antenna applications - A review abetted with patent landscape analysis.Heliyon · 2024Review
- 6G Networks and the AI Revolution-Exploring Technologies, Applications, and Emerging Challenges.Sensors (Basel, Switzerland) · 2024Review
- Surface Defect Detection of Bearing Rings Based on an Improved YOLOv5 Network.Sensors (Basel, Switzerland) · 2023Article
- Machine learning-based technique for gain and resonance prediction of mid band 5G Yagi antenna.Scientific reports · 2023Article
- Terahertz Meets AI: The State of the Art.Sensors (Basel, Switzerland) · 2023Review
- Beam-Selection for 5G/B5G Networks Using Machine Learning: A Comparative Study.Sensors (Basel, Switzerland) · 2023Article
- PredictingFrontiers in plant science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The next generation of wireless communication networks will rely heavily on machine learning and deep learning. In comparison to traditional ground-based systems, the development of various communication-based applications is projected to increase coverage and spectrum efficiency. Machine learning and deep learning can be used to optimize solutions in a variety of applications, including antennas. The latter have grown popular for obtaining effective solutions due to high computational processing, clean data, and large data storage capability. In this research, machine learning and deep learning for various antenna design applications have been discussed in detail. The general concept of machine learning and deep learning is introduced. However, the main focus is on various antenna applications, such as millimeter wave, body-centric, terahertz, satellite, unmanned aerial vehicle, global positioning system, and textiles. The feasibility of antenna applications with respect to conventional methods, acceleration of the antenna design process, reduced number of simulations, and better computational feasibility features are highlighted. Overall, machine learning and deep learning provide satisfactory results for antenna design.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.