Energy efficient placement of UAVs in wireless backhaul networks

Abstract

The enormous increase in cellular users requires novel advancements in the existing cellular infrastructure. Therefore, small cell networks (SCNs) are a promising solution to meet the ever-growing demands of cellular users as they are beneficial in terms of coverage and providing higher data rates. However, one of the challenging parts is the deployment of small cell base stations (SBs) and their connectivity with the backhaul network. In this paper, we use the scalable idea of replacing the terrestrial backhaul network with an aerial network to provide fronthaul connectivity to SBs. In particular, we address the optimum placement of unmanned aerial vehicles (UAVs) to associate the SBs such that the sum-rate of the overall network is maximized. We achieve such an objective by proposing a two-layer framework, i.e., unsupervised learning and iterative algorithm (defined as UAV equalizer), and we call this twolayer framework as a hybrid approach. Simulation results show that the proposed hybrid approach outperforms the traditional approaches in terms of maximizing the sum-rate, minimum bandwidth consumption, moreover, maximizing link utilization and energy efficiency. CCS CONCEPTS • Networks → Unmanned aerial vehicles; Small cell networks.

Backhaul-Aware Intelligent Positioning of UAVs and Association of Terrestrial Base Stations for Fronthaul Connectivity

“…Furthermore, as the child-UAVs will only be sharing the control information (i.e., SINR) with each other, therefore, GA's implementation can be viable. However, running GA at child-UAVs may reduce the hover time of a child-UAV [33], [39]; thereby, a child-UAV cannot stay aloft for a longer time period [18]. Secondly, in our network, child-UAVs will regularly be replaced with new ones; hence, running GA at child-UAVs may not be a feasible approach from the perspective of battery life.…”

“…Besides, the work presented in [14], [34]- [36] do not consider energy efficiency and height adjustment of UAVs. To deal with this, [18] addressed one-dimensional (1D), i.e., height, placement of UAVs in the backhaul networks. All the same, in [14], [18], [34]- [36], the association of TSBSs with UAVs is not evaluated by searching (in 3D) the multiple locations of UAVs, instead a fixed location of UAVs is assumed, and then the heuristic approach is used to solve the association problem.…”

“…They hover at an altitude from a few hundred meters to a few kilometers (up to a maximum of 20 kms) depending on the coverage area, users location, and weather conditions. Nevertheless, there are several challenges associated with this kind of network such as TSBSs association (serving a group of network entities, e.g., TSBSs) with the UAVs, airto-ground (ATG) channel modeling, deployment, and hover time optimization of UAVs [14]- [18]. This work focuses on the joint positioning of UAVs and the association of TSBSs by considering multiple communication-related constraints.…”

6G NR-U Based Wireless Infrastructure UAV: Standardization, Opportunities, Challenges and Future Scopes

“…In general, flying vehicles are energy-constrained systems due to their limited battery size and capacity [75]. Even after many years of development and steady improvements, the battery industry is only delivering around 25 minutes of flight time for regular multi-copters.…”

Optimal Relay Network for Aerial Remote Inspections

“…The placement of UAVs can be carried out for service-driven purposes, i.e., to improve the specific service provided by the multi-UAV system. For example, the placement of sensor UAVs can improve sensing area coverage [ 9 , 10 ], relay UAVs can improve connectivity of sensors in a wireless sensor network or in the Internet-of-Things [ 11 , 12 , 13 ], or access point UAVs can improve the coverage of users in accessing a communications infrastructure in an energy-efficient way [ 14 , 15 ]. Note that in these works, one or more UAVs provide service in parallel to other users/nodes.…”