An Image Rejection Mixer with AI-Based Improved Performance for WCDMA Applications

Yuji KASAI  Kiyoshi MIYASHITA  Hidenori SAKANASHI  Eiichi TAKAHASHI  Masaya IWATA  Masahiro MURAKAWA  Kiyoshi WATANABE  Yukihiro UEDA  Kaoru TAKASUKA  Tetsuya HIGUCHI  

IEICE TRANSACTIONS on Electronics   Vol.E89-C   No.6   pp.717-724
Publication Date: 2006/06/01
Online ISSN: 1745-1353
DOI: 10.1093/ietele/e89-c.6.717
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Section on Analog Circuit and Device Technologies)
image rejection mixer,  genetic algorithm,  WCDMA,  gain mismatch,  phase mismatch,  

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This paper proposes the combination of adjustable architecture and parameter optimization software, employing a method based on artificial intelligence (AI), to realize an image rejection mixer (IRM) that can enhance its image rejection ratio within a short period of time. The main components of the IRM are 6 Gilbert-cell multipliers. The tail current of each multiplier is adjusted by the optimization software, and the gain and phase characteristics are optimized. This adjustment is conventionally extremely difficult because the 6 tail currents to be adjusted simultaneously are mutually interdependent. In order to execute this adjustment efficiently, we employed a Genetic Algorithm (GA) that is a robust search algorithm that can find optimal parameter settings in a short time. We have successfully developed an IRM chip that has a performance of 71 dB and is suitable for single-chip integration with WCDMA applications.