Space-air-ground integrated network (SAGIN), as an integration of satellite systems, aerial networks and terrestrial communications, has been becoming an emerging architecture and attracted intensive research interest during the past years. Besides bringing significant benefits for various practical services and applications, SAGIN is also facing many unprecedented challenges due to its specific characteristics such as heterogeneity, self-organization, and time-variability. Compared to traditional ground or satellite networks, SAGIN is affected by the limited and unbalanced network resources in all three network segments, so that it is difficult to obtain the best performances for traffic delivery. Therefore, the system integration, protocol optimization, resource management and allocation in SAGIN is of great significance. To the best of our knowledge, we are the first to present the state-of-the-art of the SAGIN since existing survey papers focused on either only one single network segment in space or air, or the integration of space-ground, neglecting the integration of all the three network segments. In light of this, we present in this paper a comprehensive review of recent research works concerning SAGIN from network design and resource allocation to performance analysis and optimization. After discussing several existing network architectures, we also point out some technology challenges and future directions.

Due to the fast increase of sensing data and quick response requirement in the Internet of Things (IoT) delivery network, the high speed transmission has emerged as an important issue. Assigning suitable channels in the wireless IoT delivery network is a basic guarantee of high speed transmission. However, the high dynamics of traffic load make the conventional fixed channel assignment algorithm ineffective. Recently, the Software Defined Networking based IoT (SDN-IoT) is proposed to improve the transmission quality. Besides this, the intelligent technique of deep learning is widely researched in high computational SDN. Hence, we firstly propose a novel deep learning based traffic load prediction algorithm to forecast future traffic load and congestion in network. Then, a Deep Learning based Partially Channel Assignment Algorithm, referred to as DLPOCA, is proposed to intelligently allocate channels to each link in the SDN-IoT network. Finally, we consider a deep learning based prediction and Partially Overlapping Channel Assignment (POCA) to propose a novel intelligent channel assignment algorithm (TP-DLPOCA), which can intelligently avoid potential congestion and quickly assign suitable channels in SDN-IoT. The simulation result demonstrates that our proposal significantly outperforms conventional channel assignment algorithms.

Device-to-Device (D2D)-enabled wireless networks are becoming increasingly popular. However, in remote, rural and disaster affected areas, it is difficult to construct such wireless networks due to the unavailability or inadequacy of cellular infrastructures. Unmanned Aerial Vehicles (UAVs) can be a good candidate to promptly construct the D2D-enabled wireless network. However, the assignment of the radio channels of the nodes (i.e., UAVs and user terminals) is challenging due to the availability of only a limited number of orthogonal channels and the interference issue resulted from using arbitrary channels. Furthermore, the dynamic topology and high mobility of nodes in such a combined UAV and D2D based network make conventional Channel Assignment (CA) algorithm no longer suitable. In this paper, we formally address this problem, and demonstrate how Partially Overlapping Channels (POCs) and game theory can be exploited to alleviate the problem. In this vein, we propose a distributed Anti-Coordination game-based POC Assignment algorithm referred to as AC-POCA. In our proposed AC-POCA, the nodes use only local information to play the game, and reach a steady state, uniqueness of which is verified through analysis. Also, the upper bound of AC-POCA (i.e., Price of Anarchy) is analytically evaluated, which is corroborated by simulation results. In addition, simulation results demonstrate the effectiveness of AC-POCA in terms of good throughput and low signaling overhead in a dynamic environment.

With the exponentially growing COVID-19 (coronavirus disease 2019) pandemic, clinicians continue to seek accurate and rapid diagnosis methods in addition to virus and antibody testing modalities. Because radiographs such as X-rays and computed tomography (CT) scans are cost-effective and widely available at public health facilities, hospital emergency rooms (ERs), and even at rural clinics, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, toward automating the COVID-19 detection, in this paper, we propose a viable and efficient deep learning-based chest radiograph classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal (e.g., pneumonia) and normal cases. A unique dataset is prepared from four publicly available sources containing the posteroanterior (PA) chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our proposed DL-CRC framework leverages a data augmentation of radiograph images (DARI) algorithm for the COVID-19 data by adaptively employing the generative adversarial network (GAN) and generic data augmentation methods to generate synthetic COVID-19 infected chest X-ray images to train a robust model. The training data consisting of actual and synthetic chest X-ray images are fed into our customized convolutional neural network (CNN) model in DL-CRC, which achieves COVID-19 detection accuracy of 93.94% compared to 54.55% for the scenario without data augmentation (i.e., when only a few actual COVID-19 chest X-ray image samples are available in the original dataset). Furthermore, we justify our customized CNN model by extensively comparing it with widely adopted CNN architectures in the literature, namely ResNet, Inception-ResNet v2, and DenseNet that represent depth-based, multi-path-based, and hybrid CNN paradigms. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.

A heterogeneous computing architecture is essential to facilitate intelligent network traffic control for a joint computation, communication, and collaborative caching optimization in 6G networks to provide stringent Quality of Experience (QoE) guarantees. In this paper, we consider a 6G integrated aerial-terrestrial network model where Unmanned Aerial Vehicles (UAVs) and terrestrial Remote Radio Heads (RRHs) jointly serve as heterogeneous Base Stations (hgNBs) of a Cloud Radio Access Network (HCRAN) serving different mobile user (UE) types. We propose a distributed heterogeneous computing platform (HCP) across the UAVs and terrestrial Base Stations (BSs) by utilizing their caching and cooperative communication capabilities. In order to preserve the privacy of the content of the UEs, we propose a 2-stage federated learning algorithm among the UEs, UAVs/BSs, and HCP to collaboratively predict the content caching placement by jointly considering traffic distribution, UE mobility and localized content popularity. An asynchronous weight updating method is adopted to avoid redundant learning transfer in the federated learning. Once the global model is learnt by the HCP, it transfers the learned model to the UEs to facilitate the much desired edge intelligence in the considered 6G tiny cell. The effectiveness of the proposal is evaluated by extensive numerical analysis.

Software Defined Networking (SDN) is regarded as the next generation paradigm as it simplifies the structure of the data plane and improves the resource utilization. However, in current Software Defined Communication Systems (SDCSs), the maximum or minimum metric value based routing strategies come from traditional networks, which lack the ability of self-adaptation and do not efficiently utilize the computation resource in the controllers. To solve these problems, in this paper, we utilize the deep learning technique to conduct the routing computation for the SDCSs. Specifically, in our proposal, the considered Convolutional Neural Networks (CNNs) are adopted to intelligently compute the paths according to the input real-time traffic traces. To reduce the computation overhead of the central controller and improve the adaptation of CNNs to the changing traffic pattern, we consider an online training manner. Analysis shows that the computation complexity can be significantly reduced through the online training manner. Moreover, the simulation results demonstrate that our proposed CNNs are able to compute the appropriate paths combinations with high accuracy. Furthermore, the adopted periodical retraining enables the deep learning structures to adapt to the traffic changes.