{'fraction_non_alphanumeric': 0.025039123630672927, 'fraction_numerical': 0.001564945226917058, 'mean_word_length': 5.004694835680751, '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 Transfer Learning for 5G Massive MIMO Downlink CSI Feedback

Acquisition of downlink channel state information (CSI) is an important procedure performed at the base station (BS) for high quality wireless communication in frequency division duplexing (FDD) communication system. Generally, the downlink CSI is fed back to the BS through the user equipment (UE). Compared with traditional methods, neural network (NN) can effectively compress the downlink CSI, thus greatly reducing the feedback overhead. However, the generalization of the NN is poor, hence it is necessary to train a NN from scratch whenever there is a change in the wireless channel environment. Nevertheless, training a NN this way requires huge data and time cost in 5G massive MIMO systems. In this paper, deep transfer learning (DTL) is proposed to solve the problem of high training cost of the downlink CSI feedback NN. In a new wireless environment, our proposed technique utilises relatively small number of samples to fine-tune a pre-trained model, in order to obtain a new model with low training cost. The performance of this model is shown to be comparable with that of the NN trained with large samples. Experiment results demonstrate the effectiveness and superiority of the proposed method.