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Signal detection, identification, and characterization are among the major challenges in aerial communication systems. The ability to detect and recognize signals using cognitive technologies is still under active development when addressing uncertainties regarding signal parameters, such as blank spaces available within the transmitted signal and the utilized bandwidth. This paper proposes a learning-based identification framework for heterogeneous signals with orthogonal frequency division multiplexing (OFDM) modulation as generated in a simulated environment at an a priori unknown frequency. The implemented region-based signal identification method utilizes cyclostationary features for robust signal detection. Signal characterization is performed using a purposely-built, lightweight, region-based convolutional neural network (R-CNN). It is shown that the proposed framework is robust in the presence of additive white Gaussian noise (AWGN) and, despite its simplicity, shows better performance compared with conventional popular network architectures, such as GoogLeNet, AlexNet, and VGG 16. The signal characterization performance is validated under two degraded environments that are unknown to the system: Doppler shifted and small-scale fading. High performance is demonstrated under both degraded conditions over a wide range of signal to noise ratios (SNRs) and it is shown that the detection probability for the proposed approach is improved over those for conventional energy detectors. It is found that the signal characterization performance deteriorates under extreme conditions, such as lower SNRs and higher Doppler shifts.

To enable an efficient dynamic power and channel allocation (DPCA) for users in the downlink multi-channel non-orthogonal multiple access (MC-NOMA) systems, this paper regards the optimization as the combinatorial problem, and proposes three heuristic solutions, i.e., stochastic algorithm, two-stage greedy randomized adaptive search (GRASP), and two-stage stochastic sample greedy (SSD). Additionally, multiple complicated constraints are taken into consideration according to practical scenarios, for instance, the capacity for per sub-channel, power budget for per sub-channel, power budget for users, minimum data rate, and the priority control during the allocation. The effectiveness of the algorithms is compared by demonstration, and the algorithm performance is compared by simulations. Stochastic solution is useful for the overwhelmed sub-channel resources, i.e., spectrum dense environment with less data rate requirement. With small sub-channel number, i.e., spectrum scarce environment, both GRASP and SSD outperform the stochastic algorithm in terms of bigger data rate (achieve more than six times higher data rate) while having a shorter running time. SSD shows benefits with more channels compared with GRASP due to the low computational complexity (saves 66% running time compared with GRASP while maintaining similar data rate outcomes). With a small sub-channel number, GRASP shows a better performance in terms of the average data rate, variance, and time consumption than SSG.

Rapid increases in unmanned aerial vehicles (UAVs) applications are attributed to severe spectrum collision issues, especially when UAVs operate in spectrum scarce environments, such as urban areas. Dynamic air-to-ground (A2G) link solutions can mitigate this issue by utilizing programmable communication hardware in the air and real-time assignment of spectrum resources to achieve high-throughput and low-latency connectivity between UAVs and operators. To mitigate the high-computation issue among ground control station (GCS) networks and provide a broad communication coverage for large number of UAVs, we propose an advanced UAV A2G communication solution integrated with the dynamic spectrum management (DSM) and network function virtualization (NFV) technology to serve urban operations. The edge-cutting UAV communication technologies are surveyed. The proposed scheme is discussed in terms of the high-level system architecture, virtual network architecture, specific virtual functions (SVFs), and affiliated operation support databases. Some major research challenges are highlighted and the possible directions of future research are identified.

In anticipation of wide implementation of 5G technologies, the scarcity of spectrum resources for the unmanned aerial vehicles (UAVs) communication remains one of the major challenges in arranging safe drone operations. Dynamic spectrum management among multiple UAVs as a tool that is able to address this issue, requires integrated solutions with considerations of heterogeneous link types and support of the multi-UAV operations. This paper proposes a synthesized resource allocation and opportunistic link selection (RA-OLS) scheme for the air-to-ground (A2G) UAV communication with dynamic link selections. The link opportunities using link hopping sequences (LHSs) are allocated in the GCSs for alleviating the internal collisions within the UAV network, offloading the on-board computations in the spectrum processing function, and avoiding the contention in the air. In this context, exclusive technical solutions are proposed to form the prototype system. A sub-optimal allocation method based on the greedy algorithm is presented for addressing the resource allocation problem. A mathematical model of the RA-OLS throughput with above propositions is formulated for the spectrum dense and scarce environments. An interference factor is introduced to measure the protection effects on the primary users. The proposed throughput model approximates the simulated communication under requirements of small errors in the spectrum dense environment and the spectrum scarce environment, where the sensitivity analysis is implemented. The proposed RA-OLS outperforms the static communication scheme in terms of the utilization rate by over 50% in case when multiple links are available. It also enables the collaborative communication when the spectral resources are in scarcity. The impacts from diverse parameters on the RA-OLS communication performance are analyzed.

The main objective of the current work is to carry out the research to explore the potential wireless communication technologies that can be used during a flight operation at the airport surface for current and potential data applications in future. An important part of this work is the analysis of these services and applications from the perspective of understanding the stakeholders and communication means involved. Different communication services including both critical and non-critical ones are analyzed for aircrafts, airlines, and airport connectivity covering flight stages from landing at the airport to taking off from the airport. We are also proposing the ways of more effective use of communication means including the proposed measures for throughput improvement in order to better meet the needs of the airport stakeholders.

This paper proposes a novel learning based (LB) solution for detection and quantification of spectrum holes in periodic communications of unmanned aerial vehicles (UAVs), Instead of hypothesis testing after implementation of spectrum sensing methods, the implemented LB solution based on spectral correlation function (SCF) uses region convolutional neural network (R-CNN) for extracting quantitative parameters of the spectrum holes. The proposed LB approach is implemented using GoogLeNet architecture for the wide band detection in the scenario of orthogonal frequency division multiplexing (OFDM) communication system with the additive white Gaussian noise (AWGN) channel model. The simulation of single input single output (SISO) communication system with spectrum holes is presented. Examples of wide band detection results for both SISO and multiple input multiple output (MIMO) systems are shown and the proposed LB detector is found to be fairly accurate in identification of spectrum holes. By analyzing the training performance, the GoogLeNet architecture, along with its hyperparameter configurations and training dataset is validated. We also demonstrated that our LB detector is resilient to the AWGN environment by analyzing the precision and recall curves, average precision and mean relative error (MRE) versus signal noise ratio (SNR).

This paper addresses the alliance route network design problem considering uncertainty in the unit transportation cost. An alliance route network was constructed based on a hub-and-spoke (HS) network, in which airlines could achieve inter-area passenger transport through their international gateways. The design problem was formulated with a robust model containing a set of uncertain cost parameters. The model was established based on a three-subscript model of an HS network. A case study with real-world data was used to test the proposed model. The results showed that this robust solution can reduce the impact of cost uncertainty.

Communication in High-Frequency (HF) band allows for good-quality, low-cost, and long-distance data-link transmission over diverse landscapes in aerial communication systems. However, as limited frequency resources are allocated, HF band suffers from poor spectrum efficiency when the channel is congested with many users. To maintain the robustness of the data-link transmission, Automatic Link Establishment (ALE) is the worldwide standard for sustaining HF communication of voice, data, instant messaging, internet messaging, and image communications. Technologies, such as spectrum sensing, Dynamic Spectrum Access (DSA) are utilised in ALE with the primary step of automatic mode recognition based on cognitive radio. Conventional methods, such as Automatic Modulation Recognition (AMR) targets at the classification of single modulation, while modern communication systems require recognising multiple modes in combination of various number of tones, tone spacing, and tone interval. In this study, an approach that features filling the gap using deep learning is proposed. By characterising the common in-use mode formats in HF range, investigation shows that spectrogram diagram varies significantly, which necessitates the accurate characterisation and classification of multiple communication modes. Specifically, Convolutional Neural Network (CNN or ConvNet) is adopted for classification. The dataset is collected through USRP N210 with GNU Radio simulation. By reconstructing the communication in selected modes, the mode formats are classified. The performance result of recognition accuracy is displayed with confusion matrix. The confident classification of spectral characteristics, as well as accurate estimation, are established for practical communication scenarios.