{'fraction_non_alphanumeric': 0.03571428571428571, 'fraction_numerical': 0.017006802721088437, 'mean_word_length': 4.914572864321608, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Deep Digital Signal Recovery for LEO Satellite Communication in Presence of System Perturbations

This paper proposes a deep neural network (DNN)-based digital signal recovery (DSR) technique for low Earth orbit (LEO) satellite communications. Different from existing work, which only investigates impacts of the satellite-to-ground communication channel, this work focuses on handling the nonlinearity variations caused by input power level perturbations in the transmitter. The system is validated using a high gain radio frequency power amplifier (RF-PA) operating at 28.5 GHz, where perturbations are introduced by varying the power level of the input signal, from −39 dBm to −31 dBm, to the RF-PA. Experimental results show that the DNN trained at an input power level of −35 dBm achieved an improvement of 7.52 dB in the adjacent channel leakage ratio (ACLR), and an improvement of 4.2% in error vector magnitude (EVM). Applying the DDN trained at −35 dBm to other cases demonstrates that a 1 dB power level perturbation only leads to ≈ 1 dB degradation of the ACLR and ≈ 1.6% degradation of the EVM, respectively, which indicates the potential of the proposed approach.