A Sub-GHz Low-Power Receiver Employing Resistive-Feedback-Tuned LNA With 2.1-dB NF and 106-dB Gain Tuning Range

Cai, Liying; Lu, Zhenghao; Tan, Nianxiong; Wu, Hanming; Yu, Xiaopeng

doi:10.1109/lmwt.2022.3229684

IEEE Microw. Wireless Tech. Lett.

2023

DOI: 10.1109/lmwt.2022.3229684

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A Sub-GHz Low-Power Receiver Employing Resistive-Feedback-Tuned LNA With 2.1-dB NF and 106-dB Gain Tuning Range

Liying Cai

Zhenghao Lu

Nianxiong Tan

et al.

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2024

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An ultra-wideband current-reused LNA MMIC with negative feedback and adaptive bias networks

Xuan,

Cheng,

Gong

et al. 2024

Int. J. Microw. Wireless Technol.

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An ultra-wideband current-reused low-noise amplifier (LNA) monolithic microwave integrated circuit design is presented in this letter. Negative feedback networks are employed at both stages of the proposed LNA to expand bandwidth. Furthermore, source adaptive bias networks is designed in the first stage and combined with a current-reused construction to acquire a compact chip size and maintain low power consumption. Then, the validation of design theory is implemented by employing a 0.15-µm gallium arsenide pseudomorphic high-electron-mobility transistor process. The measured results show that the proposed LNA achieves a small signal gain of 15.5–17.8 dB, a noise figure of 3–3.65 dB, and an output 1 dB compression point (OP1 dB) of 14.5–15.5 dBm from the target bandwidth of 2–18 GHz. In addition, the fabricated LNA consumes 220 mW from a 5 V supply and occupies a chip area of 1.2 × 1.5 mm2.

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An ultra-wideband current-reused LNA MMIC with negative feedback and adaptive bias networks

Xuan,

Cheng,

Gong

et al. 2024

Int. J. Microw. Wireless Technol.

View full text Add to dashboard Cite

show abstract

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A Sub-GHz Low-Power Receiver Employing Resistive-Feedback-Tuned LNA With 2.1-dB NF and 106-dB Gain Tuning Range

Cited by 1 publication

References 16 publications

An ultra-wideband current-reused LNA MMIC with negative feedback and adaptive bias networks

An ultra-wideband current-reused LNA MMIC with negative feedback and adaptive bias networks

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