Deep learning has achieved remarkable results in modulation classification under two assumptions: a large amount of labeled class-balanced data is available, and test data shares the same distribution as data used for training. However, due to dynamic wireless fading channels and hardware imperfections, it is implausible that these assumptions hold in practice. This paper proposes Model-based Data Augmentation for Deep learning-based Modulation Classification (MDA-DMC) to build a high-quality dataset from a small amount of labeled seed data. We develop two novel augmentation methods to combat channel and hardware impairments, along with two widely used augmentation methods, adding Gaussian noise and rotation. We are the first to investigate the relationship between augmentation methods and impairments due to channel and hardware imperfections. We show that MDA- DMC can successfully combat the hardware imperfections for the same channel model as the channel model of seed data. Furthermore, with a few AWGN seed data, MDA-DMC achieves an accuracy gain of up to 17.20% in fading channels. By adding a few fading seed data, MDA-DMC improves accuracy by an additional 10.78% in fading channels.

Communication by jointly precoded transmission from many distributed access points (APs), called cell-free massive multiple-input multiple-output (CF mMIMO), is a promising concept for beyond 5G systems. One of the challenging aspects of CF mMIMO is an efficient management of the radio resources. We propose both reinforcement learning (RL)-based and heuristic precoding aware radio resource scheduling (RRS) algorithms aiming at maximizing sum spectral efficiency (SE). The proposed algorithms allocate resources for Maximum Ratio Transmission (MRT), Zero-Forcing (ZF) and Regularised Zero-Forcing (RZF) precoders. For the resource allocation, both the set of serving APs and the Physical Resource Blocks are considered. In high noise scenarios, the proposed RL-based RRS algorithm combined with the MRT precoder shows 2.4 times higher sum SE than the standard Round Robin scheduler. Moreover, we demonstrate that the proposed heuristic algorithms offer similar sum SE while significantly reducing the complexity compared to the RL-based solution. We also show that the RZF precoding, which is superior to the ZF precoding in noisy environments, results overall in more transmitted power. Therefore, assuming the same radio resource schedule and precoding strategy in the neighbouring cells, it will result in more inter-cell interference and an overall reduced performance.

Intelligent Transportation Systems (ITS) improve traffic efficiency, traffic management, driver’s comfort, and safety. They consist of a broad range of components, including vehicles, sensors, Base Stations, Road Side Units, and road infrastructure (i.e., traffic signals). ITS of the near future will need to support multi-modal transportation schemes (including ground and aerial vehicles, so-called Urban Air Mobility). ITS will have to be integrated with Unmanned Aerial Systems Traffic Management (UTM) and rely on 3 Dimensional (3D) connectivity provided by Integrated Aerial-Terrestrial 6G networks to achieve this support. In other words, various types of Unmanned Aerial Vehicles (UAVs) will become integral parts of future ITS due to their mobility, autonomous operation, and communication/processing capabilities. This article presents our view on the future integration of ITS and UTM systems, enabling wireless technologies and open research questions. We also present how UAVs can be used to enhance the performance of the currently available ITS.

In this paper, we document the current O-RAN terminology and contrast it with some common CF processing approaches.

Recently, deep learning is considered to optimize the end-to-end performance of digital communication systems. The promise of learning a digital communication scheme from data is attractive, since this makes the scheme adaptable and precisely tunable to many scenarios and channel models. In this paper, we analyse a widely used neural network architecture and show that the training of the end-to-end architecture suffers from normalization errors introduced by an average power constraint. To solve this issue, we propose a modified architecture: shifting the batch slicing after the normalization layer. This approach meets the normalization constraints better, especially in the case of small batch sizes. Finally, we experimentally demonstrate that our modified architecture leads to significantly improved performance of trained models, even for large batch sizes where normalization constraints are more easily met.

Despite several beneficial applications, unfortunately, drones are also being used for illicit activities such as drug trafficking, firearm smuggling or to impose threats to security-sensitive places like airports and nuclear power plants. The existing drone localization and neutralization technologies work on the assumption that the drone has already been detected and classified. Although we have observed a tremendous advancement in the sensor industry in this decade, there is no robust drone detection and classification method proposed in the literature yet. This paper focuses on radio frequency (RF) based drone detection and classification using the frequency signature of the transmitted signal. We have created a novel drone RF dataset using commercial drones and presented a detailed comparison between a two-stage and combined detection and classification framework. The detection and classification performance of both frameworks are presented for a single-signal and simultaneous multi-signal scenario. With detailed analysis, we show that You Only Look Once (YOLO) framework provides better detection performance compared to the Goodness-of-Fit (GoF) spectrum sensing for a simultaneous multi-signal scenario and good classification performance comparable to Deep Residual Neural Network (DRNN) framework.

Automatic Modulation Classification (AMC) receives significant interest in the context of current and future wireless communication systems. Deep learning emerged as a powerful AMC tool, as it allows for the joint learning of discriminative features, and signal classification. However, the optimization of Deep Neural Network (DNN) architectures for AMC is a manual and time-consuming process that requires profound domain knowledge and much effort. Moreover, most proposed solutions focus mainly on classification accuracy, while optimization of network complexity is neglected. In this paper, we propose a novel bi-objective memetic algorithm, BO-NSMA, to search optimal DNN architectures for AMC to maximize classification accuracy and minimize network complexity. We show that BO-NSMA, with a small initial population of six individuals and only ten generations, finds a DNN architecture that outperforms all human-crafted State-of-the-Art (SoA) models. BO-NSMA discovered the first low-complexity Convolutional Neural Network (CNN)-based model, which achieves slightly better performance than costly Recurrent Neural Network (RNN)-based approaches, allowing a 2.8-fold reduction in network complexity with 0.7% performance improvement. Compared to its counterparts from Network Architecture Search (NAS), BO-NSMA finds the best architecture, which achieves up to 18.24% accuracy gain and up to a 78.71-fold reduction in network complexity.