Efficient Placement of an Aerial Relay Drone for Throughput Maximization

Abstract

Unmanned aerial vehicle (UAV) communication can be used in overcrowded areas and either during or postdisaster situations as an evolving technology to provide ubiquitous connections for wireless devices due to its flexibility, mobility, and good condition of the line of sight channels. In this paper, a single UAV is used as an aerial relay node to provide connectivity to wireless devices because of the considerable distance between wireless devices and the ground base station. Specifically, two path loss models have been utilized; a cellular-to-UAV path loss for a backhaul connection and an air-to-ground path loss model for a downlink connection scenario. Then, the tradeoff introduced by these models is discussed. The problem of efficient placement of an aerial relay node is formulated as an optimization problem, where the objective is to maximize the total throughput of wireless devices. To find an appropriate location for a relay aerial node that maximizes the overall throughput, we first use the particle swarm optimization algorithm to find the drone location; then, we use three different approaches, namely, (1) the equal power allocation approach, (2) water filling approach, and (3) modified water filling approach to maximize the total users’ throughput. The results show that the modified water filling outperforms the other two approaches in terms of the average sum rate of all users and the total number of served users. More specifically, in the best-case scenario, it was observed that the average sum rate of the modified water filling is better than the equal power allocation and ensuring 100% coverage. In contrast, the water filling provides a very close average sum rate to the modified water filling, but it only provides a 28% user coverage.

A Virtual Full-Duplex Relaying Scheme and Adjustment Algorithms for Enhancing Spectral Efficiency in UAV-Aided Communication Networks

“…Recently, studies have been performed on the utilization of UAVs as relays [19], [20] because of the reduced cost of a UAV [3], [21] and compared to a conventional ground relay, using a UAV is advantageous owing to its mobility. These studies can be classified based on the method used to transmit and receive data.…”

Prioritized User Association for Sum-Rate Maximization in UAV-Assisted Emergency Communication: A Reinforcement Learning Approach

“…The deployment of UAVs equipped with intelligent reflecting surface (IRS) was presented in [21] to maximize the sum rate by optimizing the base stations' power allocation, phase shift of the IRS, and horizontal position of UAV using deep reinforcement Learning. The ABS efficient placement as a relay node was studied in [22] with the objective to maximize throughput. They firstly adopted a particle swarm optimization algorithm to identify the optimum ABS location, and then three different approaches were adopted to maximize throughput that involved equal power allocation, water filling, and modified water filling.…”

“…The optimal placement of UAV was achieved with minimum transmit power and minimum path loss [21] Deployment of a UAV equipped with intelligent reflecting surface (IRS) to maximize the sum rate by optimizing the power allocation of a base station (BS), phase shift of the intelligent reflecting surface (IRS), and horizontal position of the UAV Deep reinforcement learning An enhanced sum rate was obtained [22] Efficient placement of a UAV-BS serving as a relay node to maximize throughput Equal power allocation method, water filling method and modified water filling method Results showed that water filling method gives better results as compared to other two methods [23] Deployment of a UAV by optimizing its trajectory to maximize the mean opinion score (MOS)…”

Optimizing Fairness in Cellular Networks with Mobile Drone Relays

“…Our work is different, not only because we do not have drone deliveries to take into account, but also because we focus on the downlink rather than uplink, and include backhaul constraints in the problem formulation. The optimization of the relay throughput that a drone can offer is studied in [35], with a single drone providing wireless access in case of disaster, without having to account for interference and other network bottlenecks. The disaster recovery case of multiple drones connected to a single wideband base station in addressed in [36], without optimizing drone positions but rather focusing on the MAC protocol, which is optimized using a matching game.…”