Mobility management in HetNets: a learning-based perspective

Heterogeneous networks (HetNets) are expected to be a key feature of long-term evolution (LTE)-advanced networks and beyond and are essential for providing ubiquitous broadband user throughput. However, due to different coverage ranges of base stations (BSs) in HetNets, the handover performance of a user equipment (UE) may be significantly degraded, especially in scenarios where high-velocity UE traverse through small cells. In this article, we propose a context-aware mobility management (MM) procedure for small cell networks, which uses reinforcement learning techniques and inter-cell coordination for improving the handover and throughput performance of UE. In particular, the BSs jointly learn their long-term traffic loads and optimal cell range expansion and schedule their UE based on their velocities and historical data rates that are exchanged among the tiers. The proposed approach is shown not only to outperform the classical MM in terms of throughput but also to enable better fairness. Using the proposed learning-based MM approaches, the UE throughput is shown to improve by 80% on the average, while the handover failure probability is shown to reduce up to a factor of three

When Cellular Meets WiFi in Wireless Small Cell Networks

The deployment of small cell base stations(SCBSs) overlaid on existing macro-cellular systems is seen as a key solution for offloading traffic, optimizing coverage, and boosting the capacity of future cellular wireless systems. The next-generation of SCBSs is envisioned to be multi-mode, i.e., capable of transmitting simultaneously on both licensed and unlicensed bands. This constitutes a cost-effective integration of both WiFi and cellular radio access technologies (RATs) that can efficiently cope with peak wireless data traffic and heterogeneous quality-of-service requirements. To leverage the advantage of such multi-mode SCBSs, we discuss the novel proposed paradigm of cross-system learning by means of which SCBSs self-organize and autonomously steer their traffic flows across different RATs. Cross-system learning allows the SCBSs to leverage the advantage of both the WiFi and cellular worlds. For example, the SCBSs can offload delay-tolerant data traffic to WiFi, while simultaneously learning the probability distribution function of their transmission strategy over the licensed cellular band. This article will first introduce the basic building blocks of cross-system learning and then provide preliminary performance evaluation in a Long-Term Evolution (LTE) simulator overlaid with WiFi hotspots. Remarkably, it is shown that the proposed cross-system learning approach significantly outperforms a number of benchmark traffic steering policies

When Internet of Things meets Metaverse: Convergence of Physical and Cyber Worlds

In recent years, the Internet of Things (IoT) is studied in the context of the Metaverse to provide users immersive cyber-virtual experiences in mixed reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.info:eu-repo/semantics/publishedVersio

Easy Languages' success on YouTube : a video project establishes through worldwide cooperative production in the context of a social franchising concept

Die vorliegende Bachelorarbeit untersucht den Erfolg des Videoprojekts Easy Languages auf YouTube. Seit 2014 erstellt das Unternehmen mit selbigem Namen im Rahmen eines Social Franchising Modells Sprachepisoden auf YouTube in Kooperation mit der eigenen internationalen Online-Community. Durch eine weltweite Vernetzung und kostengünstiger, authentischer Produktion hat der Easy Languages YouTube-Kanal seit Januar 2014 ein starkes Wachstum verzeichnet. Dass dieses Wachstum direkt mit dem Social Franchising Modell zusammenhängt, wird in dieser Arbeit verdeutlicht. Weiterführend werden allgemeine Indikatoren und Strategien für Erfolg auf YouTube aufgezählt und mit dem YouTube-Kanal Easy Languages verglichen. Eine quantitative Datenanalyse der YouTube-Analytics des Kanals stellt darüber hinaus zusätzliche Gründe für den Erfolg des Unternehmens dar. Kann Easy Languages durch seine Alleinstellungsmerkmale eine ganz neue Richtung für Erfolg speziell für Unternehmen auf You- Tube vorgeben

Rethinking Offload: How to intelligently combine Wi-Fi and Small Cells?

International audienceAs future small cell base stations (SCBSs) are set to be multi-mode capable (i.e., transmitting on both licensed and unlicensed bands), a cost-effective integration of both technologies/systems coping with peak data demands, is crucial. Using tools from reinforcement learning (RL), a distributed cross-system traffic steering framework is proposed, whereby SCBSs autonomously optimize their long-term performance, as a function of traffic load and users' heterogeneous requirements. Leveraging the (existing) Wi-Fi component, SCBSs learn their optimal transmission strategies over both unlicensed and licensed bands. The proposed traffic steering solution is validated in a Long-Term Evolution (LTE) simulator augmented with Wi-Fi hotspots. Remarkably, it is shown that the cross-system learning-based approach outperforms several benchmark algorithms and traffic steering policies, with gains reaching up to 300% when using a traffic-aware scheduler (as compared to the classical proportional fair (PF) scheduler)