Optimizing the Energy Efficiency of Short Term Ultra Reliable Communications in Vehicular Networks

We evaluate the use of HARQ schemes in the context of vehicle to infrastructure communications considering ultra reliable communications in the short term from a channel capacity stand point. We show that it is not possible to meet strict latency requirements with very high reliability without some diversity strategy and propose a solution to determining an optimal limit on the maximum allowed number of retransmissions using Chase combining and simple HARQ to increase energy efficiency. Results show that using the proposed optimizations leads to spending 5 times less energy when compared to only one retransmission in the context of a benchmark test case for urban scenario. In addition, we present an approximation that relates most system parameters and can predict whether or not the link can be closed, which is valuable for system design

Leveraging Retransmissions in Wireless Networked Control Systems with Packetized Predictive Control

In this work we propose a communication and control joint design strategy using packetized predictive control and halted Chase combining hybrid automatic repeat request to minimize the bandwidth required to provide ultra-reliable low latency communications towards wireless networked control systems. The joint design nature comes from tunning at the same time both the length of the control horizon, a control parameter, and the maximum number of transmission attempts, a communication variable. Numerical results show that by using this strategy and correctly choosing the parameters, significant savings in bandwidth can be achieved

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

Clustering Based UAV Base Station Positioning for Enhanced Network Capacity

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