15 research outputs found
Reinforcement Learning-Based Trajectory Design for the Aerial Base Stations
In this paper, the trajectory optimization problem for a multi-aerial base
station (ABS) communication network is investigated. The objective is to find
the trajectory of the ABSs so that the sum-rate of the users served by each ABS
is maximized. To reach this goal, along with the optimal trajectory design,
optimal power and sub-channel allocation is also of great importance to support
the users with the highest possible data rates. To solve this complicated
problem, we divide it into two sub-problems: ABS trajectory optimization
sub-problem, and joint power and sub-channel assignment sub-problem. Then,
based on the Q-learning method, we develop a distributed algorithm which solves
these sub-problems efficiently, and does not need significant amount of
information exchange between the ABSs and the core network. Simulation results
show that although Q-learning is a model-free reinforcement learning technique,
it has a remarkable capability to train the ABSs to optimize their trajectories
based on the received reward signals, which carry decent information from the
topology of the network.Comment: 6 pages, 3 figures, to be presented in IEEE PIMRC 201
Drone Base Station Positioning and Power Allocation Using Reinforcement Learning
Large scale natural disasters can cause unpredictable losses of human lives and man-made infrastructure. This can hinder the ability of both survivors as well as search and rescue teams to communicate, decreasing the probability of finding survivors. In such cases, it is crucial that a provisional communication network is deployed as fast as possible in order to re-establish communication and prevent additional casualties. As such, one promising solution for mobile and adaptable emergency communication networks is the deployment of drones equipped with base stations to act as temporary small cells. In this paper, an intelligent solution based on reinforcement learning is proposed to determine the best transmit power allocation and 3D positioning of multiple drone small cells in an emergency scenario. The main goal is to maximize the number of users covered by the drones, while considering user mobility and radio access network constraints. Results show that the proposed algorithm can reduce the number of users in outage when compared to a fixed transmit power approach and that it is also capable of providing the same coverage, with lower average transmit power and using only half of the drones necessary in the case of fixed transmit power
Efficient 3D aerial base station placement considering users mobility by reinforcement learning
This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality-of-service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For fair comparison between the conventional terrestrial network and the aerial-BS assisted one, we keep the total number of BSs identical in both networks. Obtaining the best performance in such networks highly depends on the optimal placement of the aerial-BS. To this end, an algorithm which can rely on general and realistic assumptions and can decide where to go based on the past experiences is required. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find th