Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network.

No abstract

The increasing cases of wireless communication networks being partly (or even fully) destroyed after the occurrence of natural disasters has made researchers focus on the use of Unmanned Aerial Vehicles (UAVs) to provide quick and efficient backup communication in post-disaster scenarios. However, the performance of UAVs in the provisioning of wireless coverage is known to be constrained by their battery life, which limits their flight times. In this paper, we explore the use of a single UAV to provide backhaul connectivity to truck-mounted Base Stations (BSs) that have been deployed within a disaster zone to provide network coverage to users based on the principle of delay-tolerant communications. We propose a trajectory design that uses genetic algorithm to find the trajectory with the least energy requirement for the UAV to visit all the BSs and return to a central node that acts as a gateway to the core network. Our trajectory design takes into account both the straight-andlevel flight and banked-level turns of the UAV in computing the energy requirement. Simulation results show that our proposed design outperforms two approaches in the literature by up to 14% and 40%.

Unmanned aerial vehicles (UAVs) are expected to be deployed in a variety of applications in future mobile networks due to several advantages they bring over the deployment of ground base stations. However, despite the recent interest in UAVs in mobile networks, some issues still remain, such as determining the placement of multiple UAVs in different scenarios. In this paper we propose a solution to determine the optimal 3D position of multiple UAVs in a capacity enhancement use-case, or in other words, when the ground network cannot cope with the user traffic demand. For this scenario, real data from the city of Milan, provided by Telecom Italia is utilized to simulate an event. Based on that, a solution based on k-means, a machine learning technique, to position multiple UAVs is proposed and it is compared with two other baseline methods. Results demonstrate that the proposed solution is able to significantly outperform other methods in terms of users covered and quality of service.

Considering an ultra-reliable low latency communication scenario, we assess the trade-off in terms of energy consumption between achieving time diversity through retransmissions and having to communicate at a higher rate due to latency constraints. Our analysis considers Nakagami-m blockfading channels with Chase combining hybrid automatic repeat request. We derive a fixed-point equation to determine the best number of allowed transmission attempts considering the maximum possible energy spent, which yields insights into the system behavior. Furthermore, we compare the energy consumption of the proposed approach against direct transmission with frequency diversity. Results show substantial energy savings using retransmissions when selecting the maximum number of transmission attempts according to our approach. For instance, considering a Rayleigh channel and smart grid teleprotection applications, our approach uses around 8 times less energy per bit compared with a direct transmission with frequency diversity.