channel estimation; convolutional neural network (CNN); equalization; hybrid terrestrial-NTN integration; Machine learning; non-terrestrial networks (NTN); nonlinear distortion mitigation; orthogonal frequency-division multiplexing (OFDM); symbol demapping; Convolutional neural network; Demapping; Equalisation; Hybrid terrestrial-non-terrestrial network integration; Machine-learning; Network integration; Non-terrestrial network; Nonlinear distortion mitigation; Orthogonal frequency-division multiplexing; Symbol demapping; Terrestrial networks; Computer Networks and Communications; Signal Processing; Information Systems and Management; Control and Optimization; Modeling and Simulation
Abstract :
[en] This work introduces a convolutional neural network (CNN)-based receiver designed for channel estimation, equalization, and symbol demapping in orthogonal frequency-division multiplexing (OFDM) systems. The proposed receiver is specifically developed to mitigate nonlinear distortions, particularly under low input back-off (IBO), thereby markedly enhancing signal quality and energy efficiency. By significantly reducing the bit error rate (BER) and outperforming traditional methods, the CNN-based receiver establishes itself as a highly robust solution for next-generation communication technologies. These advancements are pivotal for the development of reliable and efficient communication networks, particularly in the context of the fifth generation (5G) and beyond. Furthermore, this work contributes to the integration of terrestrial and non-terrestrial networks by addressing critical challenges in mitigating nonlinear distortions in satellite and hybrid communication systems using AI-driven techniques.
This work was supported by the European Space Agency (ESA) funded under Contract No. 4000130962/20/NL/NL/FE named \"Satellite communications with AI-OFDM waveform for PAPR and ACI reduction (SAFARI)\".
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