REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association

Abstract

Unmanned Aerial Vehicles (UAVs) are emerging as important users of next-generation cellular networks. By operating in the sky, UAV users experience very different radio conditions than terrestrial users, due to factors such as strong Line-of-Sight (LoS) channels (and interference) and Base Station (BS) antenna misalignment. As a consequence, the UAVs may experience significant degradation to their received quality of service, particularly when they are moving and are subject to frequent handovers. The solution is to allow the UAV to be aware of its surrounding environment, and intelligently connect into the cellular network taking advantage of this awareness. In this paper we present REgression and deep Q-learning for Intelligent UAV cellular user to Base station Association (REQIBA), a solution that allows a UAV flying over an urban area to intelligently connect to underlying BSs, using information about the received signal powers, the BS locations, and the surrounding building topology. We demonstrate how REQIBA can as much as double the total UAV throughput, when compared to heuristic association schemes similar to those commonly used by terrestrial users. We also evaluate how environmental factors such as UAV height, building density, and throughput loss due to handovers impact the performance of our solution.

Wireless Connectivity and Localization for Advanced Air Mobility Services

“…Apart from these, the HO-related parameters such as TTT, HOM, and measurement gaps can be optimized by using AIbased techniques to meet the mobility-related performance requirements. Moreover, since the trajectories of the AVs in the drone corridors are predictable and the location of the supporting BSs can be known in prior, AI can be used to associate AVs with the BSs for reliable mobility [17]. A digital twin-based mobility and coverage study for different altitudes and BS deployments can be conducted before the initial deployment of AAM [18].…”

Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning

“…To this end, machine learning is a promising and powerful tool to provide autonomous and effective solutions to support intelligent behaviours in UAV-enabled networks [10]. Moreover, a branch of machine learning called RL has shown the capacity to address problems in UAV-enabled networks [8], [10] - [12]. The goal of RL is to learn good policies for sequential decision problems, by optimizing a cumulative future reward signal [13].…”

Optimizing Energy Efficiency in UAV-Assisted Networks Using Deep Reinforcement Learning

“…T HE DEPLOYMENT of unmanned aerial vehicles (UAVs) to provide wireless coverage to ground users has received significant research attention [1]- [7]. UAVs can play a vital role in supporting the Internet of Things (IoT) networks by providing connectivity to a large number of devices, static or mobile [1].…”

“…Nevertheless, to provide ubiquitous services to dynamic ground users, UAVs require robust strategies to optimise their flight trajectory while providing coverage. As energy-constrained UAVs operate in the sky, they may be faced with the challenge of interference from nearby UAV cells or other access points sharing the same frequency band, thereby impacting the system's energy efficiency (EE) [7].…”