2 research outputs found
Autonomous flying WiFi access point
Unmanned aerial vehicles (UAVs), aka drones, are
widely used civil and commercial applications. A promising one is
to use the drones as relying nodes to extend the wireless coverage.
However, existing solutions only focus on deploying them to
predefined locations. After that, they either remain stationary
or only move in predefined trajectories throughout the whole
deployment. In the open outdoor scenarios such as search and
rescue or large music events, etc., users can move and cluster
dynamically. As a result, network demand will change constantly
over time and hence will require the drones to adapt dynamically.
In this paper, we present a proof of concept implementation
of an UAV access point (AP) which can dynamically reposition
itself depends on the users movement on the ground. Our solution
is to continuously keeping track of the received signal strength
from the user devices for estimating the distance between users
devices and the drone, followed by trilateration to localise them.
This process is challenging because our on-site measurements
show that the heterogeneity of user devices means that change
of their signal strengths reacts very differently to the change of
distance to the drone AP. Our initial results demonstrate that
our drone is able to effectively localise users and autonomously
moving to a position closer to them
Multi-agent reinforcement learning based 3D trajectory design in aerial-terrestrial wireless caching networks
This paper investigates a dynamic 3D trajectory design of multiple cache-enabled unmanned aerial vehicles (UAVs) in a wireless device-to-device (D2D) caching network with the goal of maximizing the long-term network throughput. By storing popular content at the nearby mobile user devices, D2D caching is an efficient method to improve network throughput and alleviate backhaul burden. With the attractive features of high mobility and flexible deployment, UAVs have recently attracted significant attention as cache-enabled flying base stations. The use of cache-enabled UAVs opens up the possibility of tracking the mobility pattern of the corresponding users and serving them under limited cache storage capacity. However, it is challenging to determine the optimal UAV trajectory due to the dynamic environment with frequently changing network topology and the coexistence of aerial and terrestrial caching nodes. In response, we propose a novel multi-agent reinforcement learning based framework to determine the optimal 3D trajectory of each UAV in a distributed manner without a central coordinator. In the proposed method, multiple UAVs can cooperatively make flight decisions by sharing the gained experiences within a certain proximity to each other. Simulation results reveal that our algorithm outperforms the traditional single- and multi-agent Q-learning algorithms. This work confirms the feasibility and effectiveness of cache-enabled UAVs which serve as an important complement to terrestrial D2D caching nodes