Spatial and Temporal Management of Cellular HetNets with Multiple Solar Powered Drones

Abstract

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.Index Terms-Drone-based communications, dynamic drone small cells, energy harvesting, energy management, heterogeneous networks.

Renewable Energy-Enabled Cellular Networks

“…To overcome these drawbacks, RE, especially solar power has been considered to prolong the UAV hovering time and eventually realize perpetual flight [154], [155]. The communication with RE-powered UAVs has been addressed as an optimization problem in [49], [147]- [149]. As the most widely adopted design issues regarding the UAV communications are the placement, the trajectory, and the flight time of the UAV, these issues were jointly optimized with on/off switching of the terrestrial BSs [147] or resource allocation [49], [147]- [149] subject to the minimum QoS constraints such as the data rate of the ground user [49], [147], [148] and the received power [147].…”

Resource and Placement Optimization for Multiple UAVs using Backhaul Tethered Balloons

“…The noise power N 0 is assumed to be −110 dBm. Table I summarizes the remaining parameters that used in the simulations [4]. Fig.…”

“…In In [3], the authors investigated the trajectory optimization of one UAV, taking into consideration the energy consumption of the UAV and the users' throughput. Furthermore, the multiple UAV placement with energy management has been investigated in [4]. The goal was to minimize the overall network energy consumption by proposing an efficient switching on/off technique for terrestrial base stations, depending on the user's requirements.…”

Dynamic Self-Organizing Leader-Follower Control in a Swarm Mobile Robots System Under Limited Communication

“…To evaluate the performance of different routing protocols, [12] models the UAVs movement in a Gauss-Markov mobility model and hence is able to examine network parameters dependency. Works in [13] propose an energy management framework for cellular heterogeneous networks. In their works, the UAVs are able to determine the optimal trips of the drones and automatically to turn on/off the power, in a purpose to minimize the total energy consumption.…”