SkyCharge: Deploying Unmanned Aerial Vehicles for Dynamic Load Optimization in Solar Small Cell 5G Networks

TL;DR

The paper tackles energy-constrained 5G small-cell networks powered by solar energy, where fluctuations in harvest cause outages. It introduces SkyCharge, a hybrid framework that combines LSTM-based power forecasting with evolutionary strategies to dynamically deploy UAV-mounted base stations for load redistribution, guided by cost functions $C_phi^A$, $C_phi^U$, and $C_phi^O$. Drones act as mobile energy conduits, not direct power sources, incorporating energy terms such as $E_{BS}$, $E_{UAV}$, $E_{travel}$, and $E_{comm}$, and LOS considerations via density functions $Phi_d^A$ and $Phi_d^U$, with predictions from $P_{LSTM}$ to minimize total costs. Simulation results demonstrate substantial improvements, including an 89.2% reduction in BS outages and maintained throughput under rising user demand, indicating that the approach can enhance energy efficiency and reliability in green 5G networks and beyond.

Abstract

The power requirements posed by the fifth-generation and beyond cellular networks are an important constraint in network deployment and require energy-efficient solutions. In this work, we propose a novel user load transfer approach using airborne base stations (BS) mounted on drones for reliable and secure power redistribution across the micro-grid network comprising green small cell BSs. Depending on the user density and the availability of an aerial BS, the energy requirement of a cell with an energy deficit is accommodated by migrating the aerial BS from a high-energy to a low-energy cell. The proposed hybrid drone-based framework integrates long short-term memory with unique cost functions using an evolutionary neural network for drones and BSs and efficiently manages energy and load redistribution. The proposed algorithm reduces power outages at BSs and maintains consistent throughput stability, thereby demonstrating its capability to boost the reliability and robustness of wireless communication systems.

Figures (4)

• Figure 1: Representative system model depicting the small cell BS energy transfer mechanism between two nodes along with the drone exchange.
• Figure 2: Model architecture.
• Figure 3: Analysis of BS power outages and throughput coverage under different scenarios.
• Figure 4: Analysis of service area coverage and time between BS outages under different scenarios.