Performance Analysis of a Reconfigurable Mixer using Particle Swarm Optimization
Loading...
Files
Size: 1.37 MB, File format: Adobe PDF
Date
Authors
Mehta, shilpa
Li, Jack
Donelli, Massimo
Supervisor
Item type
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Wiley
Abstract
In this article, a novel 1.8-5 GHz downconversion mixer is presented. The mixer is designed and simulated using SiGe 8HP 130 nm CMOS process technology. The proposed mixer is implemented by incorporating a double-balanced configuration, active inductor, and current mirror techniques. For performance optimization of the proposed mixer, different algorithms such as the genetic algorithm (GA), inclined plane system optimization (IPO) algorithm, and particle swarm optimization (PSO) algorithm have been used. Compared to existing works, this design shows an enhanced conversion gain (CG), a third-order input intercept point (IIP3), and return loss () at the expense of the noise figure (NF). Additionally, the design consumes low power and covers a small chip area compared to other state-of-the-art devices. PSO shows the most promising results when compared to other optimization algorithms’ results. According to the measurement results after PSO optimization, the mixer attains a maximum CG of 25 dB, an IIP3 of 4 dBm, and a NF of 5.2 dB at 5 GHz, while consuming only 15 mW of DC power. The mixer operates at 1.2 V and covers 0.8 mm2 die area.
Description
Keywords
40 Engineering, 46 Information and Computing Sciences, 4008 Electrical Engineering, 4009 Electronics, Sensors and Digital Hardware, 4605 Data Management and Data Science, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies, Networking & Telecommunications, 4006 Communications engineering, 4009 Electronics, sensors and digital hardware
Source
International Journal of R F and Microwave Computer-Aided Engineering, ISSN: 1050-1827 (Print); 1050-1827 (Online), Wiley, 2023, 1-13. doi: 10.1155/2023/3786691
Publisher's version
Rights statement
Creative Commons Attribution License
Copyright © 2023 Shilpa Mehta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright © 2023 Shilpa Mehta et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Permanent link
Endorsement
Review
Supplemented By
Referenced By
Creative Commons license
Except where otherwise noted, this item's license is described as Creative Commons Attribution License