Cellular, Wide-Area, and Non-Terrestrial IoT: A Survey on 5G Advances and the Road Towards 6G

The next wave of wireless technologies is proliferating in connecting things among themselves as well as to humans. In the era of the Internet of things (IoT), billions of sensors, machines, vehicles, drones, and robots will be connected, making the world around us smarter. The IoT will encompass devices that must wirelessly communicate a diverse set of data gathered from the environment for myriad new applications. The ultimate goal is to extract insights from this data and develop solutions that improve quality of life and generate new revenue. Providing large-scale, long-lasting, reliable, and near real-time connectivity is the major challenge in enabling a smart connected world. This paper provides a comprehensive survey on existing and emerging communication solutions for serving IoT applications in the context of cellular, wide-area, as well as non-terrestrial networks. Specifically, wireless technology enhancements for providing IoT access in fifth-generation (5G) and beyond cellular networks, and communication networks over the unlicensed spectrum are presented. Aligned with the main key performance indicators of 5G and beyond 5G networks, we investigate solutions and standards that enable energy efficiency, reliability, low latency, and scalability (connection density) of current and future IoT networks. The solutions include grant-free access and channel coding for short-packet communications, non-orthogonal multiple access, and on-device intelligence. Further, a vision of new paradigm shifts in communication networks in the 2030s is provided, and the integration of the associated new technologies like artificial intelligence, non-terrestrial networks, and new spectra is elaborated. Finally, future research directions toward beyond 5G IoT networks are pointed out.Comment: Submitted for review to IEEE CS&

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Non-orthogonal multiple access for machine-type communications toward 6G

Abstract. Massive machine-type communications (mMTC) is one of the main focus areas in the fifth generation of wireless communications. It is also the fastest-growing field in terms of the number of devices. The massive increase in devices connected to the internet and global data traffic creates unprecedented requirements for future generations of wireless communications. One of the key technologies for the performance of the system is the utilized multiple access (MA) scheme. The conventional orthogonal MA (OMA) schemes from the earlier generations fail to satisfy the increasing demands for connectivity and spectral efficiency. On the contrary, non-orthogonal MA (NOMA) schemes offer the connectivity and spectral efficiency needed to enable mMTC. NOMA does this by allowing multiple users to transmit their data through the same resource blocks (RBs) simultaneously. NOMA is generally divided into two categories, namely power domain (PD-) NOMA and code domain (CD-) NOMA. PD-NOMA utilizes the power domain for the multiplexing, whereas CD-NOMA uses the code domain. This thesis focuses on the fundamentals of NOMA, MTC, and what NOMA can offer to MTC. We will also discuss the challenges and open problems that need to be solved. Finally, the thesis includes some simulations that demonstrate NOMA in practice.Ei-ortogonaalinen monikäyttö kone-tyyppisessä kommunikaatiossa kohti 6G:tä. Tiivistelmä. Massiivinen kone-tyyppinen kommunikaatio (mMTC) on yksi viidennen sukupolven langattoman viestinnän pääpainopisteistä. Se on myös nopeimmin kasvava osa-alue, kun katsotaan laitteiden lukumäärää. Internetiin yhdistettyjen laitteiden ja globaalin tietoliikenteen valtava kasvu luo ennennäkemättömiä vaatimuksia tuleville langattoman viestinnän sukupolville. Yksi avainteknologioista järjestelmän suorituskyvyn kannalta on käytetty monikäyttömenetelmä (MA). Tavanomaiset ortogonaaliset MA (OMA) -järjestelmät eivät saavuta yhdistettävyyden ja spektritehokkuuden kasvavia vaatimuksia. Sitä vastoin ei-ortogonaaliset MA (NOMA) -järjestelmät tarjoavat mMTC:n mahdollistamiseen tarvitun yhdistettävyyden ja spektritehokkuuden. NOMA saavuttaa tämän sallimalla usean käyttäjän lähettää dataa saman resurssilohkon kautta samanaikaisesti. NOMA voidaan yleisesti jakaa kahteen kategoriaan, tehoalueen NOMA:an ja koodialueen NOMA:an. Tämä työ keskittyy NOMA:n ja MTC:n perusteisiin ja siihen, mitä NOMA voi tarjota MTC-käyttökohteille. Työssä käydään myös läpi ratkaisuja vaativat haasteet ja avoimet ongelmat. Lopuksi työ sisältää simulaatioita, jotka mallintavat NOMA:n toimintaa käytännössä

Quantivine: A Visualization Approach for Large-scale Quantum Circuit Representation and Analysis

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations are designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://osf.io/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.Comment: Accepted by IEEE VIS 202