Neural-network-aided automatic modulation classification

Automatic modulation classification (AMC) is a pattern matching problem which significantly impacts divers telecommunication systems, with significant applications in military and civilian contexts alike. Although its appearance in the literature is far from novel, recent developments in machine learning technologies have triggered an increased interest in this area of research. In the first part of this thesis, an AMC system is studied where, in addition to the typical point-to-point setup of one receiver and one transmitter, a second transmitter is also present, which is considered an interfering device. A convolutional neural network (CNN) is used for classification. In addition to studying the effect of interference strength, we propose a modification attempting to leverage some of the debilitating results of interference, and also study the effect of signal quantisation upon classification performance. Consequently, we assess a cooperative setting of AMC, namely one where the receiver features multiple antennas, and receives different versions of the same signal from the single-antenna transmitter. Through the combination of data from different antennas, it is evidenced that this cooperative approach leads to notable performance improvements over the established baseline. Finally, the cooperative scenario is expanded to a more complicated setting, where a realistic geographic distribution of four receiving nodes is modelled, and furthermore, the decision-making mechanism with regard to the identity of a signal resides in a fusion centre independent of the receivers, connected to them over finite-bandwidth backhaul links. In addition to the common concerns over classification accuracy and inference time, data reduction methods of various types (including “trained” lossy compression) are implemented with the objective of minimising the data load placed upon the backhaul links.Open Acces

Revolutionizing Future Connectivity: A Contemporary Survey on AI-empowered Satellite-based Non-Terrestrial Networks in 6G

Non-Terrestrial Networks (NTN) are expected to be a critical component of 6th Generation (6G) networks, providing ubiquitous, continuous, and scalable services. Satellites emerge as the primary enabler for NTN, leveraging their extensive coverage, stable orbits, scalability, and adherence to international regulations. However, satellite-based NTN presents unique challenges, including long propagation delay, high Doppler shift, frequent handovers, spectrum sharing complexities, and intricate beam and resource allocation, among others. The integration of NTNs into existing terrestrial networks in 6G introduces a range of novel challenges, including task offloading, network routing, network slicing, and many more. To tackle all these obstacles, this paper proposes Artificial Intelligence (AI) as a promising solution, harnessing its ability to capture intricate correlations among diverse network parameters. We begin by providing a comprehensive background on NTN and AI, highlighting the potential of AI techniques in addressing various NTN challenges. Next, we present an overview of existing works, emphasizing AI as an enabling tool for satellite-based NTN, and explore potential research directions. Furthermore, we discuss ongoing research efforts that aim to enable AI in satellite-based NTN through software-defined implementations, while also discussing the associated challenges. Finally, we conclude by providing insights and recommendations for enabling AI-driven satellite-based NTN in future 6G networks.Comment: 40 pages, 19 Figure, 10 Tables, Surve

Review of automated time series forecasting pipelines

Time series forecasting is fundamental for various use cases in different domains such as energy systems and economics. Creating a forecasting model for a specific use case requires an iterative and complex design process. The typical design process includes the five sections (1) data pre-processing, (2) feature engineering, (3) hyperparameter optimization, (4) forecasting method selection, and (5) forecast ensembling, which are commonly organized in a pipeline structure. One promising approach to handle the ever-growing demand for time series forecasts is automating this design process. The present paper, thus, analyzes the existing literature on automated time series forecasting pipelines to investigate how to automate the design process of forecasting models. Thereby, we consider both Automated Machine Learning (AutoML) and automated statistical forecasting methods in a single forecasting pipeline. For this purpose, we firstly present and compare the proposed automation methods for each pipeline section. Secondly, we analyze the automation methods regarding their interaction, combination, and coverage of the five pipeline sections. For both, we discuss the literature, identify problems, give recommendations, and suggest future research. This review reveals that the majority of papers only cover two or three of the five pipeline sections. We conclude that future research has to holistically consider the automation of the forecasting pipeline to enable the large-scale application of time series forecasting

Novel electromagnetic design system enhancements using computational intelligence strategies

This thesis presents a wide spectrum of novel extensions and enhancements to critical components of modern electromagnetic analysis and design systems. These advancements are achieved through the use of computational intelligence, which comprises neural networks, evolutionary algorithms, and fuzzy systems. These tools have been proven in myriad industrial applications ranging from computer network optimization to heavy machinery control.The analysis module of an electromagnetic analysis and design system typically comprises mesh generation and mesh improvement stages. A novel method for discovering optimal orderings of mesh improvement operators is proposed and leads to a suite of novel mesh improvement techniques. The new techniques outperform existing methods in both mesh quality improvement and computational cost.The remaining contributions pertain to the design module. Specifically, a novel space mapping method is proposed, which allows for the optimization of response surface models. The method is able to combine the accuracy of fine models with the speed of coarse models. Optimal results are achieved for a fraction of the cost of the standard optimization approach.Models built from computational data often do not take into consideration the intrinsic characteristics of the data. A novel model building approach is proposed, which customizes the model to the underlying responses and accelerates searching within the model. The novel approach is able to significantly reduce model error and accelerate optimization.Automatic design schemes for 2D structures typically preconceive the final design or create an intractable search space. A novel non-preconceived approach is presented, which relies on a new genome structure and genetic operators. The new approach is capable of a threefold performance improvement and improved manufacturability.Automatic design of 3D wire structures is often based on "in-series" architectures, which limit performance. A novel technique for automatic creative design of 3D wire antennas is proposed. The antenna structures are grown from a starting wire and invalid designs are avoided. The high quality antennas that emerge from this bio-inspired approach could not have been obtained by a human designer and are able to outperform standard designs

Representations and representation learning for image aesthetics prediction and image enhancement

With the continual improvement in cell phone cameras and improvements in the connectivity of mobile devices, we have seen an exponential increase in the images that are captured, stored and shared on social media. For example, as of July 1st 2017 Instagram had over 715 million registered users which had posted just shy of 35 billion images. This represented approximately seven and nine-fold increase in the number of users and photos present on Instagram since 2012. Whether the images are stored on personal computers or reside on social networks (e.g. Instagram, Flickr), the sheer number of images calls for methods to determine various image properties, such as object presence or appeal, for the purpose of automatic image management and curation. One of the central problems in consumer photography centers around determining the aesthetic appeal of an image and motivates us to explore questions related to understanding aesthetic preferences, image enhancement and the possibility of using such models on devices with constrained resources. In this dissertation, we present our work on exploring representations and representation learning approaches for aesthetic inference, composition ranking and its application to image enhancement. Firstly, we discuss early representations that mainly consisted of expert features, and their possibility to enhance Convolutional Neural Networks (CNN). Secondly, we discuss the ability of resource-constrained CNNs, and the different architecture choices (inputs size and layer depth) in solving various aesthetic inference tasks: binary classification, regression, and image cropping. We show that if trained for solving fine-grained aesthetics inference, such models can rival the cropping performance of other aesthetics-based croppers, however they fall short in comparison to models trained for composition ranking. Lastly, we discuss our work on exploring and identifying the design choices in training composition ranking functions, with the goal of using them for image composition enhancement

Task-specific and interpretable feature learning

Deep learning models have had tremendous impacts in recent years, while a question has been raised by many: Is deep learning just a triumph of empiricism? There has been emerging interest in reducing the gap between the theoretical soundness and interpretability, and the empirical success of deep models. This dissertation provides a comprehensive discussion on bridging traditional model-based learning approaches that emphasize problem-specific reasoning, and deep models that allow for larger learning capacity. The overall goal is to devise the next-generation feature learning architectures that are: 1) task-specific, namely, optimizing the entire pipeline from end to end while taking advantage of available prior knowledge and domain expertise; and 2) interpretable, namely, being able to learn a representation consisting of semantically sensible variables, and to display predictable behaviors. This dissertation starts by showing how the classical sparse coding models could be improved in a task-specific way, by formulating the entire pipeline as bi-level optimization. Then, it mainly illustrates how to incorporate the structure of classical learning models, e.g., sparse coding, into the design of deep architectures. A few concrete model examples are presented, ranging from the $\ell_0$ and $\ell_1$ sparse approximation models, to the $\ell_\infty$ constrained model and the dual-sparsity model. The analytic tools in the optimization problems can be translated to guide the architecture design and performance analysis of deep models. As a result, those customized deep models demonstrate improved performance, intuitive interpretation, and efficient parameter initialization. On the other hand, deep networks are shown to be analogous to brain mechanisms. They exhibit the ability to describe semantic content from the primitive level to the abstract level. This dissertation thus also presents a preliminary investigation of the synergy between feature learning with cognitive science and neuroscience. Two novel application domains, image aesthetics assessment and brain encoding, are explored, with promising preliminary results achieved