Manageable Fast Handover at Access Point in WLAN

The handover speed is always vital for the further development of Wireless Local Area Network (WLAN), which is enjoying a fast growth. Based on the handover technology specified in IEEE 802.11 WLAN, Manageable Fast Handover (MFHO) mechanism is proposed to speed up handover at the Access Point (AP), meet handover demands of services with different Quality of Service (QoS), and ensure service continuity. Adopting a handover policy named 'Make-before-break', this mechanism enables wireless APs to control and manage handover between two stations based on improving Inter-Access Point Protocol (IAPP). Tests have been carried out to compare functions and performance of MFHO and IAPP-based handover technology. The test results prove that MFHO provides a higher successful handover ratio and better handover performance than IAPP-based handover technology.

Machine learning in vehicular networking:An overview

As vehicle complexity and road congestion increase,combined with the emergence of electric vehicles,the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential.The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks.Particularly,5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities.Machine Learning(ML),a powerful methodology for adaptive and predictive system development,has emerged in both vehicular and conventional wireless networks.Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions,such as traditional control loop design and optimization techniques.This article provides a short survey of ML applications in vehicular networks from the networking aspect.Research topics covered in this article include network control containing handover management and routing decision making,resource management,and energy efficiency in vehicular networks.The findings of this paper suggest more attention should be paid to network forming/deforming decision making.ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies,such as mobile edge computing for real-world deployment.Research datasets,simulation environment standardization,and method interpretability also require more research attention.

Reinforcement Learning-Based Optimization for Drone Mobility in 5G and Beyond Ultra-Dense Networks

Drone applications in 5th generation(5G)networks mainly focus on services and use cases such as providing connectivity during crowded events,human-instigated disasters,unmanned aerial vehicle traffic management,internet of things in the sky,and situation awareness.4G and 5G cellular networks face various challenges to ensure dynamic control and safe mobility of the drone when it is tasked with delivering these services.The drone can fly in three-dimensional space.The drone connectivity can suffer from increased handover cost due to several reasons,including variations in the received signal strength indicator,co-channel interference offered to the drone by neighboring cells,and abrupt drop in lobe edge signals due to antenna nulls.The baseline greedy handover algorithm only ensures the strongest connection between the drone and small cells so that the drone may experience several handovers.Intended for fast environment learning,machine learning techniques such as Q-learning help the drone fly with minimum handover cost along with robust connectivity.In this study,we propose a Q-learning-based approach evaluated in three different scenarios.The handover decision is optimized gradually using Q-learning to provide efficient mobility support with high data rate in time-sensitive applications,tactile internet,and haptics communication.Simulation results demonstrate that the proposed algorithm can effectively minimize the handover cost in a learning environment.This work presents a notable contribution to determine the optimal route of drones for researchers who are exploring UAV use cases in cellular networks where a large testing site comprised of several cells with multiple UAVs is under consideration.