Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance-broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2\% for the binary classification) and can overcome the overfitting problem.Accurate flight delay prediction is fundamental to establish the more efficient airline business. Recent studies have been focused on applying machine learning methods to predict the flight delay. Most of the previous prediction methods are conducted in a single route or airport. This paper explores a broader scope of factors which may potentially influence the flight delay, and compares several machine learning-based models in designed generalized flight delay prediction tasks. To build a dataset for the proposed scheme, automatic dependent surveillance-broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition, flight schedule, and airport information. The designed prediction tasks contain different classification tasks and a regression task. Experimental results show that long short-term memory (LSTM) is capable of handling the obtained aviation sequence data, but overfitting problem occurs in our limited dataset. Compared with the previous schemes, the proposed random forest-based model can obtain higher prediction accuracy (90.2\% for the binary classification) and can overcome the overfitting problem.

Characterization and linearization of RF power amplifiers (PAs) are key issues of fifth-generation wireless communication systems, especially when high peak-to-average ratio waveforms are introduced. Recently, deep learning methods have achieved great success in numerous domains including wireless physical-layer. However, there has been limited work in using deep learning for PAs behavioral modeling and linearization. In this paper, we make a bridge between memory effects of the nonlinear PAs and memory of bidirectional long short-term memory (BiLSTM) neural networks. We then build a BiLSTM-based behavioral modeling architecture and its accompanying digital predistortion (DPD) model by reconciling a non-causality concern. Next, an additional model is proposed in this study to mitigate uncertainty of the tested PA when transforming phases. The experimental results demonstrate the effectiveness of the proposed scheme, in which the adequately trained networks are capable of characterizing the PA, and the artificial intelligence-based DPD shows promising linearization performance when considering the tested PAs inherent unpredictability.

Timely and efficient air traffic flow management (ATFM) is a key issue in future dense air traffic. The emerging demands for unmanned aerial vehicles and general aviation aircraft aggravate the burden of the ATFM. Thanks to the advanced automatic dependent surveillance-broadcast (ADS-B) technique, the aerial vehicles can be tracked and monitored in a real-time and accurate manner, providing possibility for establishing a more intelligent ATFM architecture. In this paper, we first form an aviation big data platform by using the distributed ADS-B ground stations and the obtained ADS-B messages. By exploring the constructed dataset and mapping the extracted information to the routes, the air traffic flow between different cities can be counted and predicted, where the prediction task is implemented on the basis of two machine learning methods, respectively. The experimental results based on real-world data demonstrate that the proposed traffic flow prediction model adopting long short-term memory (LSTM) can achieve better performance, especially when abnormal factors in traffic control are considered.

In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).

Automatic modulation classification (AMC) is considered as one of the most critical technologies for the non-cooperative communication systems. Recently, deep learning (DL) based AMC (DL-AMC) methods have been attracted intensely attentions due to their preferable performance. However, most of DL-AMC methods are concentrated in the single-input and single-output (SISO) systems, while there are few works on DL-based AMC methods in multiple-input and multiple-output (MIMO) systems. Hence, we propose a convolutional neural network (CNN) and zero-forcing equalization-based AMC (ZF-AMC) method for MIMO systems. Simulation results demonstrate that the CNN-based ZF-AMC method achieves better performance than the artificial neural network (ANN) and high order cumulants (HOC)-based AMC method under the condition of the perfect channel statement information (CSI). Moreover, we also explore the impact of the imperfect CSI on the performance of the ZF-AMC method, due to its difficult acquisition of the perfect CSI. Simulation results demonstrated that the classification performance is not only influenced by the channel error, but also associated with the number of the transmitted and received antennas.

This paper aims to improve spectrum efficiency (SE) for the unmanned aerial vehicle (UAV) relayed cellular uplinks, through distinguishing both line of sight (LoS) and non-line of sight (NLoS) links. Meanwhile, aiming to accommodate the air-to-ground (A2G) cooperative non-orthogonal multiple access (NOMA) based cellular users (CUs) with a high energy efficiency (EE), a joint resource allocation (RA) problem is further considered for the UAV and the CUs. To solve the problem, firstly an access-priority based receiver determination (RD) method is derived. According to the RD result, heuristic user association (UA) strategies are given. Then, based on the UA result, transmission powers of the CUs and the UAV are initialized based on their quality of service (QoS) demands. Further, the subchannels are assigned to the associated CUs and the UAV with the reweighted message-passing algorithm. At last, the transmission power of the CUs and the UAV is jointly fine-tuned with the proposed access control schemes. Compared with the traditional orthogonal frequency division multiple access (OFDMA) scheme and the traditional ground-to-ground (G2G) NOMA scheme, simulation results confirm that the UAV aided NOMA with the proposed joint RA scheme yields better performances in terms of the SE, the EE and the access ratio of the CUs.