Computationally optimized multi‐port antenna systems for WLAN/Wi‐Fi (IEEE 802.11a/h/j/n/ac/ax), 5G (mid‐band), and UWB applications

Summary This paper presents computationally optimized 2‐element and 4‐element Multiple‐Input, Multiple‐Output (MIMO) antennas for WLAN/Wi‐Fi, 5G, and UWB applications. The antenna configuration is constructed with the orthogonal placement of sawtooth‐shaped circular monopole radiating elements. The Particle Swarm Optimization (PSO) and Covariance Matrix Adaptation Evolution Strategy (CMA‐ES) optimization techniques are employed to achieve the best performance and size of the proposed antenna. Among these optimization techniques, CMA‐ES is identified as the better approach. The final optimized geometry of the 2‐element and 4‐element MIMO antennas is 49.40 mm × 24.22 mm and 49.44 mm × 45 mm, respectively. Both optimized antennas are fabricated and experimentally verified. The fractional bandwidth of the antenna is more than 110.3%, and more than −20 dB isolation is attained without employing any decoupling method. The Envelop Correlation Coefficient (ECC), Directivity Gain (DG), Total Active Reflection Coefficient (TARC), and Channel Capacity Limit (CCL) are 0.0001, 9.99, < −15 dB, and 0.1 bits/s/Hz, respectively. The proposed antenna is a good candidate for numerous current wireless applications due to its size and performance.

Authors

Institutions

Publication Details

Journal
International Journal of Communication Systems
Published
2024-09-08
DOI
https://doi.org/10.1002/dac.5985
Citations
5
Primary Topic
Antenna Design and Analysis
Type
article
Field-Weighted Citation Impact
6.49
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Computationally optimized multi‐port antenna systems for WLAN/Wi‐Fi (IEEE 802.11a/h/j/n/ac/ax), 5G (mid‐band), and UWB applications

Satya P. Singh, Pradnya A. Gajbhiye, Madan Kumar Sharma
5 citations
International Journal of Communication Systems
Antenna Design and Analysis
6.49
article

Computationally optimized multi‐port antenna systems for WLAN/Wi‐Fi (IEEE 802.11a/h/j/n/ac/ax), 5G (mid‐band), and UWB applications

Satya P. Singh, Pradnya A. Gajbhiye, Madan Kumar Sharma
article en
5 citations

Abstract

Summary This paper presents computationally optimized 2‐element and 4‐element Multiple‐Input, Multiple‐Output (MIMO) antennas for WLAN/Wi‐Fi, 5G, and UWB applications. The antenna configuration is constructed with the orthogonal placement of sawtooth‐shaped circular monopole radiating elements. The Particle Swarm Optimization (PSO) and Covariance Matrix Adaptation Evolution Strategy (CMA‐ES) optimization techniques are employed to achieve the best performance and size of the proposed antenna. Among these optimization techniques, CMA‐ES is identified as the better approach. The final optimized geometry of the 2‐element and 4‐element MIMO antennas is 49.40 mm × 24.22 mm and 49.44 mm × 45 mm, respectively. Both optimized antennas are fabricated and experimentally verified. The fractional bandwidth of the antenna is more than 110.3%, and more than −20 dB isolation is attained without employing any decoupling method. The Envelop Correlation Coefficient (ECC), Directivity Gain (DG), Total Active Reflection Coefficient (TARC), and Channel Capacity Limit (CCL) are 0.0001, 9.99, < −15 dB, and 0.1 bits/s/Hz, respectively. The proposed antenna is a good candidate for numerous current wireless applications due to its size and performance.

International Journal of Communication SystemsVol. 38(3)
Netaji Subhas University of Technology (IN), University of Buraimi (OM)
Openalex Percentile: Top 4%
Antenna Design and Analysis
6.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.