A deep learning receiver for non-linear transmitter

Non-linearity of wireless transceivers, specifically power amplifier (PA) non-linearity, could pose major limitations towards having high throughput, and cost and energy efficient wireless communication systems. Such limitations from the PA are typically compensated in the transmitter, e.g. by applying power back-off or performing digital-pre-distortion (DPD) aiming to linearize the transmitter. However, applying PA power back-off leads to lower energy efficiency and lower output power, and hence lower coverage; and performing DPD results in higher complexity of the transmitters. This paper presents an alternative approach based on a receiver method to perform signal detection in the presence of distortions due to PA non-linearity.

Research paper

#6G #ANN #Hexa-X

We propose a receiver technique using artificial neural networks (ANN) to compensate for the PA non-linearity at the receiver side. The paper presents link-level simulation results using pre-trained neural network models based on synthesized training data. The simulation results confirm that the designed receiver can tolerate higher distortions, hence allow the PA output power back-off to be reduced, leading to higher output power improving coverage, spectral efficiency, energy efficiency, and throughput.

Available in IEEEXplore DOI: 10.1109/ACCESS.2023.3234501

Authors

Hamed Farhadi, Johan Haraldson, Mårten Sundberg – Ericsson Research

Published in IEEE Access ( Volume: 11).

Open Access, shared under a Creative Commons License

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A deep learning receiver for non-linear transmitter

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