{'fraction_non_alphanumeric': 0.033444816053511704, 'fraction_numerical': 0.01137123745819398, 'mean_word_length': 4.960159362549801, '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 Learning-Based Energy Optimization for Edge Device in UAV-Aided Communications

Edge devices (EDs) carry limited energy, but 6th generation mobile networks (6G) communication will consume more energy. The unmanned aerial vehicle (UAV)-aided wireless communication network can provide communication links to EDs without a signal. However, with the time-lag system, the EDs cannot dynamically adjust the emission energy because the dynamic UAV coordinates cannot be accurately acquired. In addition, the fixed emission energy makes the EDs have poor endurance. To address this challenge, in this paper, we propose a deep learning-based energy optimization algorithm (DEO) to dynamically adjust the emission energy of the ED so that the received energy of the mobile relay UAV is, as much as possible, equal to the sensitivity of the receiver. Specifically, the edge server provides the computing platform and uses deep learning (DL) to predict the location information of the relay UAV in dynamic scenarios. Then, the ED emission energy is adjusted according to the predicted position. It enables the ED to communicate reliably with the mobile relay UAV at minimum energy. We analyze the performance of a variety of predictive networks under different time-delay systems through experiments. The results show that the Weighted Mean Absolute Percentage Error (WMAPE) of this algorithm is 0.54%, 0.80% and 1.15% under the effect of a communication delay of 0.4 s, 0.6 s and 0.8 s, respectively.