arXiv subjects

Zhiqiang Qi

Publications and source records attributed to Zhiqiang Qi.

4 recordsLinked to original sources

arXiv2023

5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul

Fast and reliable wireless communication has become a critical demand in human life. In the case of mission-critical (MC) scenarios, for instance, when natural disasters strike, providing ubiquitous connectivity becomes challenging by using traditional wireless networks. In this context, unmanned aerial vehicle (UAV) based aerial networks offer a promising alternative for fast, flexible, and reliable wireless communications. Due to unique characteristics such as mobility, flexible deployment, and rapid reconfiguration, drones can readily change location dynamically to provide on-demand communications to users on the ground in emergency scenarios. As a result, the usage of UAV base stations (UAV-BSs) has been considered an appropriate approach for providing rapid connection in MC scenarios. In this paper, we study how to control multiple UAV-BSs in both static and dynamic environments. We use a system-level simulator to model an MC scenario in which a macro BS of a cellular network is out of service and multiple UAV-BSs are deployed using integrated access and backhaul (IAB) technology to provide coverage for users in the disaster area. With the data collected from the system-level simulation, a deep reinforcement learning algorithm is developed to jointly optimize the three-dimensional placement of these multiple UAV-BSs, which adapt their 3-D locations to the on-ground user movement. The evaluation results show that the proposed algorithm can support the autonomous navigation of the UAV-BSs to meet the MC service requirements in terms of user throughput and drop rate.

cs.NI↗

arXiv2022

Towards Providing Connectivity When and Where It Counts: An Overview of Deployable 5G Networks

Public safety operations require fast and reliable mission critical communications under various scenarios, in which the availability of wireless connectivity can be a question of life or death. To provide connectivity when and where it counts, we have witnessed a growing demand for deployable networks for public safety in natural disasters or emergency situations. This article investigates the opportunities of using the 5th generation (5G) new radio (NR) standard for designing flexible and reliable deployable networks. We describe use cases and provide an overview of deployable 5G network concepts, including architecture options, system performance analysis, and coexistence aspects. We also identify technical challenges that can be considered in the evolution of 5G NR to unlock the full potential of deployable 5G networks.

cs.NI↗

arXiv2022

Autonomous Navigation and Configuration of Integrated Access Backhauling for UAV Base Station Using Reinforcement Learning

Fast and reliable connectivity is essential to enhancing situational awareness and operational efficiency for public safety mission-critical (MC) users. In emergency or disaster circumstances, where existing cellular network coverage and capacity may not be available to meet MC communication demands, deployable-network-based solutions such as cells-on-wheels/wings can be utilized swiftly to ensure reliable connection for MC users. In this paper, we consider a scenario where a macro base station (BS) is destroyed due to a natural disaster and an unmanned aerial vehicle carrying BS (UAV-BS) is set up to provide temporary coverage for users in the disaster area. The UAV-BS is integrated into the mobile network using the 5G integrated access and backhaul (IAB) technology. We propose a framework and signalling procedure for applying machine learning to this use case. A deep reinforcement learning algorithm is designed to jointly optimize the access and backhaul antenna tilt as well as the three-dimensional location of the UAV-BS in order to best serve the on-ground MC users while maintaining a good backhaul connection. Our result shows that the proposed algorithm can autonomously navigate and configure the UAV-BS to improve the throughput and reduce the drop rate of MC users.

cs.LG↗

arXiv2021

Deployable Networks for Public Safety in 5G and Beyond: A Coverage and Interference Study

Deployable networks are foreseen to be one of the key technologies for public safety in fifth generation (5G) mobile communications and beyond. They can be used to complement the existing public cellular networks to provide temporary and on-demand connectivity in emergency situations. However, operating deployable networks in coexistence with public cellular networks can be challenging from an interference perspective. To gain insights on the deployment strategy for deployable networks, in this article, we present an extensive numerical study of coverage and interference analysis, considering four different co-existence scenarios and different types of deployable base stations (BSs), i.e., BS on a truck and BS on an Unmanned Aerial Vehicle (UAV). Our simulation results show that deploying deployable BSs in rural scenarios can provide good coverage to meet the service requirement for mission critical (MC) users. In addition, the interference impact is only substantial when the deployable and public networks are close to each other. Finally, allowing the MC users to access the public network can be of vital importance to guarantee their service when the interference level between public and deployable network is very high.

cs.NI↗