{'fraction_non_alphanumeric': 0.025380710659898477, 'fraction_numerical': 0.0, 'mean_word_length': 5.444444444444445, '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 Data Hiding-based CSI Feedback Overhead Elimination: An Initial Investigation

The downlink channel state information (CSI) feedback occupies substantial precious transmission resources in frequency-division duplexing (FDD) systems. In this work, we propose a data hiding-based CSI feedback framework, namely, EliCsiNet, to eliminate the CSI feedback overhead in FDD systems with deep learning. The key idea of this work is to hide downlink CSI within the transmitted messages (e.g., images) with no transmission resource occupation and few effects on the message semantic. We propose a novel neural network framework, in which the user extracts and hides the CSI features within the images by networks, and the base station recovers the CSI from the transmitted images. Simulation results demonstrate that the proposed EliCsiNet framework can eliminate the CSI feedback overhead with few effects on the transmitted images, including the image quality and classification accuracy.