AI-augmented intelligent RFICs for transmitter predistortion for 5G and 6G wireless and space communication applications

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Signal bandwidths have been increasing five times in every generation from 2G, reaching 100 MHz in 5G. Digital Predistortion (DPD) techniques were introduced in 3G and have been used since to reduce the impairments in RF front-ends, allowing the use of more energy efficient components and modes of operation at the cost of additional computation in baseband. As a result, the energy consumption of 3G base stations were reduced by up to 10%.In current DPD technology, the signal processing and transmitter components operate at few times the signal bandwidth to carry the correction information along with the signal information to the RF front-end. With wider signal bandwidths, 5G DPD processing complexity and energy consumption have increased to a point that offsets the energy efficiency gains in the RF frontend. Moreover, a full receiver is needed to monitor the RF front-end and adapt to its behavioural changes. This baseband-RF-baseband loop is complex, expensive, and slow in following the system changes. With the use of beamforming techniques, 5G+ Front-ends operating conditions vary rapidly and current DPD technology is unable to track these fast changes.This project aims to design an artificial intelligence (AI)-augmented RFIC predistorted front-end with the ability to quickly adapt to changes in the RF front-end environment and operating conditions. By including sensors to monitor the RF front-end environmental and operating conditions changes, and an AI engine to adapt the predistorter to these changes, the proposed solution will reduce the signal processing complexity, energy consumption, and latency. This solution would be the first truly intelligent RF system that is aware of its environment and able to adapt to changes. A proof-of-concept RFIC will be designed using state-of-the-art Gallium Nitride (GaN) nano technology and fabricated in world-leading nanofab facilities.