Neural network-based nonlinear distortion correction of power amplifiers for UAV/Drone video transmittion channel
Keywords:
Digital predistortion, Neural networks, Power amplifier (PA), Orthogonal frequency division multiplexing (OFDM), UAV/Drone video linkAbstract
Correcting for nonlinearities of radio frequency (RF) power amplifiers (PAs) is one of the most important issues of autonomic wireless communication systems, such as portable systems, wireless video link transmitter in unmanned aerial vehicles (UAVs), OcuSync transmission system utilised on drones Mavic Pro, and etc., because system performance is largely dependent on PAs. Recently, neural networks (NNs) have achieved great success in PAs behavioral modeling and linearization due to their excellent approximation capability. This paper presents a NN based digital predistortion (DPD) using the realistic video link transmit signals captured from a Doherty PA. Its effectiveness has been validated through extensive simulations and experiments for linearizing a nonlinear Doherty PA with strong memory effect. The results show that an adjacent channel leakage ratio (adjacent channel leakage ratio) correction in excess of 20 dB is observed for the video link transmit signals and the proposed NN-based DPD can extend the linear range of the tested PA up to 4.2 dB when controlling the signals to go into deep compression region close to saturation.
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