Researchers at the Tokyo Institute of Technology (Tokyo Tech) and NEC Corporation in Japan have developed a 39 GHz transceiver with built-in calibration for 5G applications. The advantages to be gained include better quality communications as well as cost-effective scalability. A team of more than 20 researchers has successfully demonstrated this 39 GHz transceiver that could be used in the next wave of 5G wireless equipment including base stations, smartphones, tablets, and Internet-of-Things (IoT) applications.
The new transceiver is based on a 64-element (4 x 16) phased-array design. Its built-in gain phase calibration means that it can improve beamforming accuracy, and thereby reduce undesired radiation and boost signal strength.
Fabricated in a standard 65-nanometer CMOS process, the transceiver's low-cost silicon-based components make it ideal for mass production - a key consideration for accelerated deployment of 5G technologies.
The researchers showed that the built-in calibration has a very low root-mean-square (RMS) phase error of 0.08°. This figure is an order of magnitude lower than previous comparable results. While transceivers developed to date typically suffer from high gain variation of more than 1 dB, the new model has a maximum gain variation of just 0.04 dB over the full 360° tuning range.
The transceiver has a maximum equivalent isotropic radiated power (EIRP) of 53 dBm. This is an impressive indication of the output power of the 64 antennas, the researchers say, particularly for low-cost CMOS implementation.
Indoor testing (under anechoic chamber conditions), which involved a one-meter, over-the-air measurement, demonstrated that the transceiver supports wireless transmission of a 400 MHz signal with 64QAM.
The product was presented at the 2019 IEEE Radio Frequency Integrated Circuits Symposium (RFIC) in Boston, Massachusetts, US, as part of the morning session (Session RTu2E) held on 4 June 2019. The paper of this work "A 39 GHz 64-Element Phased-Array CMOS Transceiver with Built-in Calibration" by Yun Wang et al., received the best student paper award.
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